Generating forecasted emissions value modifications and monitoring for physical emissions sources utilizing machine-learning models

ABSTRACT

Methods, systems, and non-transitory computer readable storage media are disclosed for generating action recommendations for generating action recommendations for modifying physical emissions sources of an entity based on forecasting and monitoring emissions production for the entity utilizing machine-learning models. Specifically, the disclosed system forecasts emissions produced by an entity by utilizing a plurality of different forecasting machine-learning models corresponding to different physical emissions sources to generate forecasted source attributes. Additionally, the disclosed system combines the forecasted source attributes to generate a plurality of forecasted emissions value modifications for a future time period. The disclosed system generates action recommendations for modifying the physical emissions sources based on the forecasted emissions value modifications. In additional embodiments, the disclosed system tracks emissions of the entity during the future time period and generate additional action recommendations in response to detecting deviations from forecasted emissions production.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. Pat. Application No.17/651,388, filed on Feb. 16, 2022, which claims the benefit of andpriority to U.S. Provisional Pat. Application No. 63/262,200, filed Oct.7, 2021. Each of the aforementioned applications is hereby incorporatedby reference in its entirety.

BACKGROUND

Increases in prevalence of technological and manufacturing processesover recent decades-in addition to increasing population numbers-haveled to increasing levels of greenhouse gas emissions, leading to arapidly changing climate. As a result, many countries and organizationsare increasing emissions measuring and reporting regulations for variousentities based on internal and external operations of the entities.Because many entities (even small businesses) generate substantialamounts of emissions of various types from potentially hundreds ofdifferent sources, determining overall emissions from previous timeperiods can be a very complex and difficult problem. Furthermore,determining future emissions based on growth or other changes to entityoperations given the number of emission types and sources given variousconstraints is also challenging. Given the emergent nature of emissionsstandards and reporting, conventional systems are unable to monitoremissions from large numbers of sources while also modeling futureemissions under a number of different constraints.

SUMMARY

This disclosure describes one or more embodiments of methods,non-transitory computer readable media, and systems that solve theforegoing problems (in addition to providing other benefits) bygenerating action recommendations for modifying physical emissionssources of an entity based on forecasting and monitoring emissionsproduction for the entity utilizing machine-learning models.Specifically, the disclosed systems forecast emissions produced by anentity by utilizing a plurality of different forecastingmachine-learning models corresponding to different physical emissionssources to generate forecasted source attributes and usage.Additionally, the disclosed systems combine the forecasted sourceattributes to generate a plurality of forecasted emissions valuemodifications for a future time period. The disclosed systems generateaction recommendations for modifying the physical emissions sourcesbased on the forecasted emissions value modifications. In additionalembodiments, the disclosed systems track emissions of the entity duringthe future time period and generate additional action recommendations inresponse to detecting deviations from forecasted emissions production.In some embodiments, the disclosed systems also utilize the actionrecommendations to modify the physical emissions sources. The disclosedsystems thus utilize a plurality of machine-learning models toefficiently, accurately, and flexibly monitor and forecast emissionsproduction of an entity for one or more future time periods.

BRIEF DESCRIPTION OF THE DRAWINGS

Various embodiments will be described and explained with additionalspecificity and detail through the use of the accompanying drawings inwhich:

FIG. 1 illustrates an example of a system environment in which anemissions optimizer system, an emissions forecasting system, and anemissions monitoring system can operate in accordance with one or moreimplementations.

FIG. 2 illustrates an example of an overview of a process of theemissions optimizer system utilizing a modified gradient descent modelto generate action recommendations for modifying physical emissionssources in accordance with one or more implementations.

FIG. 3 illustrates an example of a detailed process of the emissionsoptimizer system utilizing a modified gradient descent model to generateaction recommendations for modifying physical emissions sources inaccordance with one or more implementations.

FIG. 4 illustrates an example of the emissions optimizer systemutilizing a modified gradient descent model to iteratively adjustemissions values for physical emissions sources in accordance with oneor more implementations.

FIG. 5 illustrates an example of the emissions optimizer systemutilizing generating natural language action recommendations formodifying emissions values in accordance with one or moreimplementations.

FIGS. 6A-6B illustrate examples of graphical user interfaces includinggraphical user interface elements for setting a plurality of constraintsand a plurality of target emissions values in accordance with one ormore implementations.

FIG. 7 illustrates examples of sampled data points generated by theemissions optimizer system utilizing a modified gradient descent modelin accordance with one or more implementations.

FIGS. 8A-8F illustrate chart diagrams of past data and modeled data forphysical emissions sources corresponding to an entity in accordance withone or more implementations.

FIG. 9 illustrates an example of an overview of a process of theemissions forecasting system utilizing a plurality of forecastingmachine-learning models to generate action recommendations for modifyingphysical emissions sources with one or more implementations.

FIG. 10 illustrates an example of a detailed process of the emissionsforecasting system utilizing a plurality of forecasting machine-learningmodels to generate action recommendations for modifying physicalemissions sources in accordance with one or more implementations.

FIGS. 11A-11C illustrate examples of the emissions forecasting systemperforming operations for generating forecasted emissions valuemodifications utilizing machine-learning models corresponding tospecific physical emissions sources and the emissions modificationsystem performing operations for monitoring emissions by an entity inaccordance with one or more implementations.

FIGS. 12A-12C illustrate chart diagrams of past data and forecasted datafor physical emissions sources corresponding to an entity in accordancewith one or more implementations.

FIGS. 13A-13B illustrate examples of graphical user interfaces includinggraphical user interface elements for presenting action recommendationsutilizing forecasting machine-learning models and monitoring progress ofan entity in accordance with one or more implementations.

FIG. 14 illustrates a flowchart of a series of acts for generatingaction recommendations for modifying physical emissions sources based onforecasted emissions usage utilizing a plurality of forecastingmachine-learning models in accordance with one or more implementations.

FIG. 15 illustrates a block diagram of an exemplary computing device inaccordance with one or more embodiments.

DETAILED DESCRIPTION

This disclosure describes one or more embodiments of an emissionsforecasting system that utilizes a plurality of forecastingmachine-learning models to generate action recommendations based onforecasted emissions usage of an entity and an emissions monitoringsystem to monitor performance relative to forecasted emissions usage. Inone or more embodiments, the emissions forecasting system utilizessource-specific forecasting machine-learning models to generateforecasted source attributes and usage of the physical emissions sourcesfor a future time period according to a set of constraints andhistorical data associated with the entity. The emissions forecastingsystem utilizes the forecasted source attributes to determine forecastedemissions value modifications for the physical emissions sources.Additionally, the emissions forecasting system generates actionrecommendations to provide to the entity for modifying the physicalemissions sources and reduce emissions produced by the entity and usageof the physical emissions sources according to the forecasted data. Inadditional embodiments, the emissions monitoring system monitorsemissions generated by the entity during the future time period andprovides action recommendations to correct deviations from theforecasted emissions production. The emissions monitoring systemutilizes historical data in connection with the forecasted data toprovide real-time alerts.

As mentioned, in one or more embodiments, the emissions forecastingsystem generates forecasted source attributes for a plurality ofphysical emissions sources corresponding to an entity for one or morefuture time periods. Specifically, the emissions forecasting systemdetermines forecasting machine-learning models for the physicalemissions sources based on attributes of historical data associated withthe physical emissions sources. The emissions forecasting systemutilizes the selected forecasting machine-learning models to generateforecasted source attributes for the physical emissions sources based onconstraints and historical data associated with the entity. In someembodiments, the emissions forecasting system generates the forecastedsource attributes based on results provided by an emissions optimizersystem according to specific target emissions values for the entity.

In one or more embodiments, the emissions forecasting system determinesforecasted emissions value modifications from the forecasted sourceattributes. For instance, the emissions forecasting system utilizes amodel that generates the forecasted emissions value modifications byweighting the forecasted source attributes generated by the individualforecasted machine-learning models. Additionally, the emissionsforecasting system generates the forecasted emissions valuemodifications based on the constraints and/or emissions valuemodifications generated by the emissions optimizer system.

According to one or more embodiments, the emissions forecasting systemgenerates action recommendations for modifying physical emissionssources corresponding to an entity based on forecasted emissions valuemodifications. In particular, the emissions forecasting systemdetermines forecasted emissions value modifications for one or more timeperiods and generates action recommendations for meeting forecastedemissions production or usage of physical emissions sources. Inadditional embodiments, the emissions forecasting system also utilizesconstraints associated with the entity to generate the actionrecommendations consistent with goals and/or limitations for the entitysuch as emissions or cost goals.

Furthermore, in some embodiments, the emissions monitoring systemmonitors emissions produced by an entity for comparison to forecastedemissions. To illustrate, the emissions monitoring system tracksemissions produced by physical emissions sources corresponding to theentity during a future time period. The emissions monitoring systemdetermines whether the tracked emissions deviate from a plan (e.g., theforecasted emissions value modifications) corresponding to the physicalemissions sources. In response to detecting deviations, the emissionsmonitoring system generates one or more additional actionrecommendations to correct for the deviations.

As mentioned, conventional systems have a number of shortcomings inrelation to managing and modeling emissions associated with entityoperations. For example, some conventional systems for controlling theoperations of physical emissions sources rely on tools that track datasuch as inventory, labor, or other aspects of entity operations. Whilesuch conventional systems provide useful insights regarding suchemissions, the conventional systems are unequipped to configureemissions sources for compliance with recent emissions standards or tomanage emissions measuring and reporting according to recent emissionsstandards. Due to the inability of conventional systems to track ormodel emissions sources and emissions production, entities attempting tocontrol the operations of emissions sources consistently withoperational goals via conventional systems must manually monitoremissions sources. Given the large number of physical emissions sources(and different types of emissions sources) and other variables involvedwith tracking and modeling emissions for even small entities, however,manually tracking and/or predicting emissions via conventional systemsis inefficient and inaccurate.

The disclosed emissions optimizer system, emissions forecasting system,and emissions monitoring system provide a number of advantages overconventional systems. For example, the emissions forecasting systemprovides flexibility for computing systems that control operations ofphysical emissions sources by tracking and modeling emissions producedby large numbers of various physical emissions sources for an entity. Inparticular, in contrast to conventional systems that are unable toconfigure emissions sources (thus requiring manual monitoring andconfiguration by entities), the emissions forecasting systemautomatically tracks and forecasts usage and/or emissions values forpast and future time periods for different types of entities withdifferent emissions sources. To illustrate, by managing an entity’semissions consistent with other operational data of the entity, theemissions optimizer system provides up-to-date, detailed emissions datathat allows entity’s to easily generate a plan for reducing emissions.The emissions optimizer system also provides optimal parameters for anentity’s business or financial constraints while achieving specifiedemissions and cost goals. In addition, the emissions optimizer system isable to automatically determine whether a solution is possible given thevarious constraints and goals and suggests various modifications to theconstraints or goals to obtain a solution. Additionally, the emissionsforecasting system also provides additional flexibility by forecastingemissions data for different datasets and various combinations of futuretime periods. The emissions monitoring system also detects deviations ofemissions from forecast goals on a regular basis based on data collectedfrom users and/or other systems. By evaluating emissions values based onthe data and comparing the data with the forecast goals, the entitymapping system is able to quickly provide updated recommendations forcorrecting the deviations in emissions usage.

Furthermore, the emissions optimizer system, emissions forecastingsystem, and emissions monitoring system also improves efficiency ofcomputing systems for controlling operations of emissions sources.Specifically, the emissions forecasting system utilizes a plurality ofsource-specific forecasting machine-learning models to accurately andefficiently forecast source attributes for large numbers of emissionssources for applying modifications to operations of emissions sourcesfor future time periods. For example, the emissions forecasting systemutilizes a plurality of machine-learning models to generate forecastedemissions data for various physical emissions sources according toweights (e.g., contribution proportions) corresponding to the physicalemissions sources. Additionally, the emissions forecasting systemgenerates action recommendations for implementing emissions valuemodifications to specific physical emissions sources while takingadditional variables (e.g., target emissions values and variousconstraints) into account that otherwise significantly increase thecomplexity of an optimization process with conventional systems.Furthermore, the emissions monitoring system monitors deviations from aforecasted plan to provide updated/adjusted recommendations or alerts inresponse to determining that an entity changes its emissions usage overtime relative to target emissions usage.

Additionally, the emissions optimizer system, the emissions forecastingsystem, and the emissions monitoring system also provides improvedaccuracy for computing systems that control emissions sources. Forexample, the emissions optimizer system provides configuration of aplurality of physical emissions sources by utilizing a multi-variableobjective algorithm (e.g., a mixed-integer programming algorithm such asa modified gradient descent model) to iteratively process emissionsvalues for a plurality of emissions sources given defined constraintsand one or more target emissions values. The emissions forecastingsystem also selects machine-learning models to use for a plurality ofdifferent physical emissions sources based on the availability, type, orother attributes of historical data associated with the physicalemissions sources. Accordingly, the emissions forecasting system selectsappropriate models for accurately generating forecasted emissions datafor the different physical emissions sources based on the availabledata. The emissions forecasting system thus accurately determinesspecific actions for modifying operations of physical emissions sourcesto achieve specific goals while complying with the various constraintsin a number of different scenarios. Additionally, even if the entitymisses target emissions usage for a portion of a future time period, theemissions monitoring system utilizes real-time monitoring of theentity’s emissions usage for providing alerts and updated accurateforecasting to account for deviations.

Turning now to the figures, FIG. 1 includes an embodiment of a systemenvironment 100 in which an emissions forecasting system 102 and anemissions monitoring system 103 are implemented. In particular, thesystem environment 100 includes server device(s) 104 and a client device106 in communication via a network 108. Moreover, as shown, the serverdevice(s) 104 include an entity management system 110, which includesthe emissions forecasting system 102, the emissions monitoring system103, and an emissions optimizer system 112. As further illustrate inFIG. 1 , the emissions optimizer system 112 includes a modified gradientdescent model 114. Furthermore, the emissions forecasting system 102includes a plurality of forecasting machine-learning models 116.Additionally, the client device 106 includes an entity managementapplication 118, which optionally includes the entity management system110, the emissions forecasting system 102 (including the forecastingmachine-learning models 116), and the emissions optimizer system 112(including the modified gradient descent model 114). In additionalembodiments, as illustrated in FIG. 1 , the system environment 100includes a third-party database 120, which includes emissions data 122.In further embodiments, the system environment 100 includes a sourcemodification device 126, which manages operations for physical emissionssources 128.

As shown in FIG. 1 , in one or more implementations, the serverdevice(s) 104 includes or hosts the entity management system 110.Specifically, the entity management system 110 includes, or is part of,one or more systems that implement management of entity operations. Forexample, the entity management system 110 provides tools for generating,viewing, or otherwise interacting with operational data (e.g.,inventory, labor, emissions data) associated with an entity. Toillustrate, the entity management system 110 communicates with theclient device 106 via the network 108 to provide the tools for displayand interaction via the entity management application 118 at the clientdevice 106. Additionally, in some embodiments, the entity managementsystem 110 receives data from the client device 106 in connection withmanaging operational data associated with the entity, including requeststo perform operations based on digital content stored at the serverdevice(s) 104 (or at another device such as a source repository) and/orrequests to store digital content from the client device 106 at theserver device(s) 104 (or at another device). In some embodiments, theentity management system 110 receives interaction data for generating orviewing operational data based on digital content (e.g., emissionssource data 124) at the client device 106, processes the interactiondata (e.g., to generate or edit operational data), and provides theresults of the interaction data to the client device 106 for display viathe entity management application 118 or to a third-party system.

In one or more embodiments, the entity management system 110 providestools for generating operational data (including emissions data) for anentity. In particular, the entity management system 110 provides tools(e.g., via the entity management application 118) for selecting,viewing, or generating emissions data or action recommendationscorresponding to the emissions data. Additionally, the entity managementsystem 110 utilizes the emissions optimizer system 112 to intelligentlygenerate action recommendations for modifying physical emissions sourcescorresponding to an entity based on the emissions source data 124provided by the client device. The emissions optimizer system 112 alsoutilizes a database (e.g., the third-party database 120) includingemissions data 122 (e.g., based on a standard emissions protocol) fordetermining emissions values corresponding to the emissions source data124. For example, the emissions optimizer system 112 utilizes themodified gradient descent model 114 to iteratively adjust emissionsvalues based on the emissions source data 124 according to on one ormore target values. Furthermore, in one or more embodiments, theemissions optimizer system 112 utilizes the modified gradient descentmodel 114 to generate the action recommendations based on a plurality ofconstraints provided to the entity management system 110 (e.g., from theclient device 106).

In additional embodiments, the entity management system 110 utilizes theemissions forecasting system 102 to intelligently forecast emissionsproduction for an entity. Specifically, the emissions forecasting system102 utilizes the plurality of forecasting machine-learning models 116 togenerate forecasted emissions value modifications for a plurality ofphysical emissions sources corresponding to the entity. By forecastingemissions production of the entity for one or more future time periodsutilizing the forecasting machine-learning models 116, the emissionsforecasting system 102 generates an emissions plan (e.g., via one ormore action recommendations) for modifying physical emissions sources.

In one or more embodiments, after the emissions optimizer system 112 andthe emissions forecasting system 102 generate action recommendations formodifying physical emissions sources associated with an entity, theentity management system 110 provides the action recommendations to theclient device 106 for display. For instance, the entity managementsystem 110 sends the action recommendations to the client device 106 viathe network 108 for display via the entity management application 118.Additionally, the emissions forecasting system 102 and/or the emissionsmonitoring system103 can receive additional inputs to apply additionalchanges to the emissions source data 124, constraints, and/or targetemissions values or to update forecasted data. To illustrate, the clientdevice 106 can provide tracked emissions data corresponding to theentity during a time period for the emissions monitoring system 103 toutilize in detecting deviations from forecasted data. The entitymanagement system 110 utilizes the emissions optimizer system 112, theemissions forecasting system 102, and/or the emissions monitoring system103 to generate additional action recommendations based on the updatedemissions source data 124, constraints, and/or target emissions valuesor for additional forecasted data.

According to one or more embodiments, the entity management system 110,the emissions optimizer system 112, the emissions forecasting system102, the emissions monitoring system 103, and/or the client device 106provide instructions for implementing one or more actions based on theaction recommendations to the source modification device 126 (or aplurality of source modification devices). To illustrate, in response toa user interaction via the client device 106 to select one or moreaction recommendations, the client device 106, the emissions optimizersystem 112, the emissions forecasting system 102, or the emissionsmonitoring system 103 sends instructions to the source modificationdevice 126 to perform one or more corresponding operations for modifyingthe physical emissions sources 128. The source modification device 126performs the operation(s) by modifying the physical emissions sources128, such as by establishing/modifying control limits that limitoperations of one or more physical emissions sources (e.g., settingautomatic time limits, turning on/off specific sources, restricting usebased on time/usage thresholds, controlling gas/electricity flow, travelbudget availability for employees).

In additional embodiments, the server device(s) 104 provide sourcemodification instructions directly to the source modification device 126such that the source modification device 126 automatically applies themodifications to the physical emissions sources 128. Accordingly, thesource modification device 126 includes devices or machinery that modifyoperations associated with the physical emissions sources 128. In one ormore embodiments, the source modification device 126 includes acomputing device (or other physical control device including aprocessor) for executing instructions related to controlling thephysical emissions sources 128.

Specifically, in one or more embodiments, the emissions optimizer system112 sends instructions to the source modification device 126 (acontroller, a central processing device, a thermostat, etc.) to modifyoperations of a physical emissions source 128 (e.g., an oven, an HVACsystem, a furnace, a boiler, a water heater, light bulbs, etc.). Forexample, the emissions optimizer system 112 or the emissions monitoringsystem 103 sends instructions to source modification device 126 to limitoperation of a physical emissions source 128 to certain hours during theday, to a certain number of hours a day, or to stay within one or moreoperating parameters (e.g., minimum/maximum temperature, minimum/maximumspeed, minimum/maximum power).

In one or more embodiments, the server device(s) 104 include a varietyof computing devices, including those described below with reference toFIG. 15 . For example, the server device(s) 104 includes one or moreservers for storing and processing data associated with operationaldata, emissions data, and action recommendations for modifying physicalemissions sources for an entity. In some embodiments, the serverdevice(s) 104 also include a plurality of computing devices incommunication with each other, such as in a distributed storageenvironment. In some embodiments, the server device(s) 104 include acontent server. The server device(s) 104 also optionally includes anapplication server, a communication server, a web-hosting server, asocial networking server, a digital content campaign server, or adigital communication management server.

In addition, as shown in FIG. 1 , the system environment 100 includesthe client device 106. In one or more embodiments, the client device 106includes, but is not limited to, a mobile device (e.g., smartphone ortablet), a laptop, a desktop, including those explained below withreference to FIG. 15 . Furthermore, although not shown in FIG. 1 , theclient device 106 can be operated by a user (e.g., a user included in,or associated with, the system environment 100) to perform a variety offunctions. In particular, the client device 106 performs functions suchas, but not limited to, accessing, viewing, and interacting with digitalcontent (e.g., emissions source data, emissions data, actionrecommendations). In some embodiments, the client device 106 alsoperforms functions for generating, capturing, or accessing data toprovide to the entity management system 110 and the emissions optimizersystem 112 in connection with entity management. For example, the clientdevice 106 communicates with the server device(s) 104 via the network108 to provide information (e.g., user interactions) associated withgenerating action recommendations. Although FIG. 1 illustrates thesystem environment 100 with a single client device 106, in someembodiments, the system environment 100 includes a different number ofclient devices.

Additionally, as shown in FIG. 1 , the system environment 100 includesthe network 108. The network 108 enables communication betweencomponents of the system environment 100. In one or more embodiments,the network 108 may include the Internet or World Wide Web.Additionally, the network 108 can include various types of networks thatuse various communication technology and protocols, such as a corporateintranet, a virtual private network (VPN), a local area network (LAN), awireless local network (WLAN), a cellular network, a wide area network(WAN), a metropolitan area network (MAN), or a combination of two ormore such networks. Indeed, the server device(s) 104 and the clientdevice 106 communicates via the network using one or more communicationplatforms and technologies suitable for transporting data and/orcommunication signals, including any known communication technologies,devices, media, and protocols supportive of data communications,examples of which are described with reference to FIG. 15 .

Although FIG. 1 illustrates the server device(s) 104 and the clientdevice 106 communicating via the network 108, in alternativeembodiments, the various components of the system environment 100communicate and/or interact via other methods (e.g., the serverdevice(s) 104 and the client device 106 can communicate directly).Furthermore, although FIG. 1 illustrates the emissions optimizer system112 and the emissions forecasting system 102 being implemented by aparticular component and/or device within the system environment 100,the emissions optimizer system 112 and/or the emissions forecastingsystem 102 can be implemented, in whole or in part, by other computingdevices and/or components in the system environment 100 (e.g., theclient device 106).

In particular, in some implementations, the emissions optimizer system112 on the server device(s) 104 supports the emissions optimizer system112, the emissions forecasting system 102, and the emissions monitoringsystem 103 on the client device 106. For instance, the emissionsoptimizer system 112, the emissions forecasting system 102, and/or theemissions monitoring system 103 on the server device(s) 104 generates ortrains the emissions optimizer system 112 (e.g., the modified gradientdescent model 114), the emissions forecasting system 102 (e.g., theforecasting machine-learning models 116), and/or the emissionsmonitoring system 103 for the client device 106. The server device(s)104 provides the generated/trained emissions optimizer system 112 and/orthe generated/trained emissions forecasting system 102 to the clientdevice 106. In other words, the client device 106 obtains (e.g.,downloads) the emissions optimizer system 112, the emissions forecastingsystem 102, and/or the emissions monitoring system 103 from the serverdevice(s) 104. At this point, the client device 106 is able to utilizethe emissions optimizer system 112, the emissions forecasting system102, and/or the emissions monitoring system 103 to generateoperational/emissions data and action recommendations independently fromthe server device(s) 104.

In alternative embodiments, the emissions optimizer system 112, theemissions forecasting system 102, and/or the emissions monitoring system103 includes a web hosting application that allows the client device 106to interact with content and services hosted on the server device(s)104. To illustrate, in one or more implementations, the client device106 accesses a web page supported by the server device(s) 104. Theclient device 106 provides input to the server device(s) 104 to performemissions data and action recommendation generation operations, and, inresponse, the emissions optimizer system 112, the emissions forecastingsystem 102, the emissions monitoring system 103, or the entitymanagement system 110 on the server device(s) 104 performs operations togenerate emissions data and action recommendations. The server device(s)104 provide the output or results of the operations to the client device106.

As mentioned, the emissions optimizer system 112 utilizes dataindicating emissions produced by an entity to generate actionrecommendations for modifying one or more physical emissions sources.FIG. 2 illustrates an overview of the emissions optimizer system 112processing physical emissions source data 200 to generate actionrecommendations 202. Specifically, the emissions optimizer system 112utilizes the modified gradient descent model 114 to generate emissionsvalues modifications 204 from the physical emissions source data 200.The emissions optimizer system 112 generates the action recommendations202 from the emissions values modifications 204.

In one or more embodiments, the emissions optimizer system 112determines the physical emissions source data 200 in connection with aplurality of physical emissions sources for an entity. For example, thephysical emissions source data 200 includes a number and a type of eachof a plurality of physical emissions sources corresponding to theentity. FIG. 2 illustrates that the physical emissions source data 200includes data associated with a plurality of physical emissions source206 a-206 n. To illustrate, the physical emissions source data 200includes a number of units of a first physical emissions source 206 a.The physical emissions source data 200 can also include a source type ofthe first physical emissions source 206 a.

In one or more embodiments, a physical emissions source (or “emissionssource”) includes an object, substance, or action that produces physicalemissions. For instance, a physical emissions source includes actionssuch as, but not limited to, objects, substances, or actions related totravel by employees of an entity or delivery drivers utilizingtransportation vehicles (e.g., cars, trucks, airplanes) that the entitymay or may not own. In additional examples, a physical emissions sourceincludes objects or substances such as, but not limited to, utilities(e.g., electricity, natural gas, water) on properties owned or used byan entity, vehicles owned or used by an entity, gases or fuels used byfurnaces or heating elements, cooking tools such as stoves or ovens,manufacturing tools including assembly lines or individual parts of anassembly line, or agricultural byproducts that generate physicalemissions.

According to one or more embodiments, emissions (or “physicalemissions”) include specific substances generated or produced by one ormore sources. For example, emissions include specific gases or liquids.To illustrate, the emissions optimizer system 112 determines emissionsthat are categorized as greenhouse gases that absorb and emit radiantenergy within a thermal infrared range and are correlated with (orcause) the greenhouse effect in relation to climate change.Specifically, physical emissions include various factors such as, butnot limited to, carbon dioxide, methane, nitrous oxide, water vapor, orozone. Additionally, in one or more embodiments, the emissions optimizersystem 112 determines various climate change factors based on physicalemissions recognized in emissions standards including, but not limitedto, a CO2 factor, a CH4 factor, a N2O factor, a BIO CO2 factor, an AR4(CO2e) factor, and an AR5 (CO2e) factor.

Furthermore, in one or more embodiments, the emissions optimizer system112 utilizes the modified gradient descent model 114 to generate theemissions values modifications 204 based on adjustments to emissionsvalues corresponding to the physical emissions source data 200.Specifically, as described in more detail with respect to FIG. 3 andFIG. 4 , the emissions optimizer system 112 utilizes the modifiedgradient descent model 114 to iteratively adjust emissions values fordifferent physical emissions sources to attempt to achieve one or moretarget emissions values. Additionally, the emissions optimizer system112 generates the action recommendations 202 based on the emissionsvalues modifications 204 to provide to the entity.

FIG. 3 illustrates a diagram of a detailed process of the emissionsoptimizer system generating action recommendations for modifyingphysical emissions sources corresponding to an entity. In particular,the emissions optimizer system 112 utilizes a modified gradient descentmodel 300 to determine one or more actions that the entity may performto achieve one or more emissions goals given various constraints. Forexample, the emissions optimizer system 112 utilizes the modifiedgradient descent model 300 to generate action recommendations 302 tomodify physical emissions for meeting the emissions goals.

As illustrated in FIG. 3 , the emissions optimizer system 112 determinesphysical emissions source data 304 for physical emissions sourcescorresponding to an entity. For example, the emissions optimizer system112 determines unit numbers 304 a indicating a number of units of eachphysical emissions source type. To illustrate, the emissions optimizersystem 112 determines how many delivery drivers are associated with theentity, how many miles the delivery drivers drive during a given timeperiod (e.g., daily, monthly, or yearly), a number of cooking ormanufacturing units are associated with the entity, etc. The emissionsoptimizer system 112 thus determines how many units of a given emissionssource the entity uses (or is associated with) during operations of theentity.

In addition to the unit numbers 304 a, the emissions optimizer system112 also determines source categories 304 b corresponding to theplurality of physical emissions sources. In some embodiments, eachsource category produces a specific amount of emissions of one or moreemission types. For instance, the emissions optimizer system 112determines a source category for each physical emissions source based ona source type of the physical emissions source. To illustrate, theemissions optimizer system 112 determines a first source category for afirst physical emissions source, a second source category for a secondphysical emissions source, etc. In additional embodiments the emissionsoptimizer system 112 determines a plurality of different physicalemissions sources for a single source category. Accordingly, theemissions optimizer system 112 assigns a corresponding source categoryto each unit of a particular type of physical emissions source.

According to one or more embodiments, the emissions optimizer system 112determines emissions values 306 based on the physical emissions sourcedata 304. Specifically, the emissions optimizer system 112 accesses anemissions database 308 including data for determining how the emissionsproduction of each unit of a particular physical emissions source. Toillustrate, the emissions optimizer system 112 accesses the emissionsdatabase 308 from a third-party system that determines emissions valuesaccording to a standard emissions protocol (e.g., a greenhouse gasprotocol “GHG”). In some embodiments, the emissions optimizer system 112the emissions database 308 includes data indicating emissions values ofa plurality of emission types for each unit of each source category.Thus, the emissions optimizer system 112 determines total emissionsvalues produced by the physical emissions sources corresponding to theentity by utilizing the unit numbers 304 a, the source categories 304 b,and the emissions database 308.

In one or more embodiments, the emissions optimizer system 112 alsodetermines constraints 310 in connection with modifying physicalemissions sources for an entity. In particular, the constraints 310include indications of requirements or limitations that determineboundaries for modifying physical emissions sources. As illustrated inFIG. 3 , the constraints 310 include source constraints 310 a, budgetconstraints 310 b, and additional constraints 310 c. For instance, thesource constraints 310 a indicate requirements of numbers or types ofphysical emissions sources (e.g., a minimum unit number of one or morephysical emissions sources or source categories). In one or moreembodiments, the source constraints 310 a indicate that an entity hasgoals that require a certain number of units of one or more emissionssources. Additionally, the source constraints 310 a can include businessconstraints related to expansion plans for meeting futuresales/operational targets that the entity does not want to compromise(e.g., expanding from 10 locations to 20 locations within 2 years).

In one or more embodiments, the budget constraints 310 b includefinancial requirements of operations. For example, the budgetconstraints 310 b indicate that an entity has certain financialcapabilities for implementing changes related to reducing emissions. Toillustrate, the budget constraints 310 b can include one or more budgetlimitations for adding or replacing physical emissions sources, such asa budget limitation for replacing a limited number of gas poweredvehicles with electric vehicles.

In some embodiments, the additional constraints 310 c include otherconstraints not covered by the source constraints 310 a or the budgetconstraints 310 b. Specifically, an entity may have certain operationsor actions that the entity does not want to compromise. For instance, anentity may have a certain amount of travel that entity leadership oremployees are required to perform within a specific time period thatlimits the amount of travel reduction available for reducing emissions.The additional constraints 310 c can also indicate constraints based onobligations that the entity has with one or more other entities.

In one or more embodiments, the emissions optimizer system 112determines the physical emissions source data 304, the constraints 310,and/or the target emissions values 312 based on user-defined values. Forexample, the emissions optimizer system 112 determines the physicalemissions source data 304, the constraints 310, and/or the targetemissions values 312 based on user input provided via one or more clientdevices associated with the entity. In some instances, the emissionsoptimizer system 112 also utilizes default values for the physicalemissions source data 304, the constraints 310, and/or the targetemissions values 312.

In alternative embodiments, the emissions optimizer system 112automatically determines the physical emissions source data 304, theconstraints 310, and/or the target emissions values 312. To illustrate,the emissions optimizer system 112 utilizes a machine-learning modelthat processes entity data (e.g., operations data) indicating detailsassociated with the entity. The emissions optimizer system 112determines the physical emissions source data 304, the constraints 310,and/or the target emissions values 312 by estimating numbers of physicalemissions sources, future/target physical emissions sources, and/ortarget emissions values. The emissions optimizer system 112 can alsoutilize data associated with similar entities to generate estimates ofthe physical emissions source data 304, the constraints 310, and/or thetarget emissions values 312.

For example, the emissions optimizer system 112 utilizes a neuralnetwork (e.g., a convolutional neural network, recurrent neural network,deep neural network) to generate features representing an entity and aplurality of additional entities (e.g., based on the entity data). Theneural network can determine a similarity between the entity andadditional entities (e.g., via entity/feature matching). In one or moreembodiments, the emissions optimizer system 112 determines physicalemissions source data, constraints, and/or target emissions values forthe entity based on one or more similar entities.

In one or more embodiments, the emissions optimizer system 112 utilizesthe neural network to determine a similarity between the entity and oneor more additional entities. For instance, the emissions optimizersystem 112 obtains a plurality of attributes of each entity including,but not limited to, entity size, entity type, entity profits/expenses,location, or operations data. The emissions optimizer system 112utilizes the neural network to encode the attributes (and any learnedrelationships among the attributes) to generate feature vectorsrepresenting the entities. The emissions optimizer system 112 determinessimilar entities based on distances between the feature vectors (e.g.,based on the distances between feature vectors in a feature space). Inone or more implementations, the emissions optimizer system 112determines that the smaller the distance between feature vectors in thefeatures space the greater the similarity between the entitiesrepresented by the feature vectors.

In response to determining one or more similar entities to the entity,the emissions optimizer system 112 determines the physical emissionssource data, constraints, and/or target emissions values for the entitybased on entity data associated with the similar entity/entities. Inparticular, the emissions optimizer system 112 retrieves entity datafrom a similar entity and determines corresponding data for an entitybased on the retrieved data. To illustrate, in response to determiningthat a first entity has a similar entity size and entity type as asecond entity, the emissions optimizer system 112 utilizes the neuralnetwork to determine missing data or estimated data associated with thefirst entity based on retrieved data for the second entity. In addition,the emissions optimizer system 112 can determine missing/estimated data(or modifications to the entity data) associated with the first entityby averaging corresponding data from a plurality of similar entities(e.g., a weighted average of data from the N most similar entities basedon corresponding feature representations). In some instances, theemissions optimizer system 112 also compares the entity data for thefirst entity to similar entities and notifies the first entity inresponse to detecting significant deviations from similar entities(e.g., indicating a possible error in the entity data).

As illustrated in FIG. 3 , the emissions optimizer system 112 alsodetermines target emissions values 312 for modifying physical emissionssources corresponding to an entity. In one or more embodiments, theemissions optimizer system 112 determines the target emissions values312 based on emissions goals for a future time period for the entity.For example, the emissions optimizer system 112 determines thatemissions goals for reducing emissions produced by the entity by aspecific amount/percentage within a specific amount of time (e.g., -20%emissions within two years). In some embodiments, the emissionsoptimizer system 112 thus determines the target emissions values 312based on the emissions values 306 and the emissions goals for the entity(e.g., based on one or more percentages of the emissions values 306).

After determining the emissions values 306, the constraints 310, and thetarget emissions values 312 the emissions optimizer system 112 utilizesthe modified gradient descent model 300 to generate the actionrecommendations 302. Specifically, the emissions optimizer system 112utilizes the modified gradient descent model 300 to iteratively adjustthe emissions values 306 corresponding to the physical emissions sourcestoward the target emissions values 312. Furthermore, the emissionsoptimizer system 112 utilizes the modified gradient descent model 300 toadjust the emissions values 306 while meeting the constraints 310.

As mentioned, the number of variables involved in adjusting emissionsvalues for large numbers of physical emissions sources of differenttypes and given various constraints can be very large. To illustrate,even small entities can be associated with tens or hundreds of physicalemissions sources, while large entities can be associated with tens ofthousands or hundreds of thousands of physical emissions sources.Accordingly, optimizing variables for such large numbers of variables isimpractical (or even impossible) utilizing conventional manual methods(e.g., via spreadsheet tools) given current software/hardwarelimitations. Additionally, adjusting certain emissions values (orcorresponding physical emissions sources) can affect other emissionsvalues or violate one or more constraints during optimization, resultingin a complex emissions optimization problem. The emissions optimizersystem 112 thus utilizes the modified gradient descent model 300 togenerate an emissions reduction plan 314 including a plurality ofemissions values modifications 314 a-314 n. For example, the emissionsoptimizer system 112 generates a first emission values modification 314a for modifying a first physical emissions source (or source category),a second emissions values modification 314 b for modifying a secondphysical emissions source (or source category), etc. Each emissionsvalues modification includes a plan to meet a specific number of unitsof a particular physical emissions source for meeting the targetemissions values.

In one or more additional embodiments, the modified gradient descentmodel 300 also determines whether the target emissions values arepossible given the emissions values 306 and the constraints 310. Inparticular, an entity may have established constraints and/or targetemissions values that are incompatible with each other. Accordingly, theemissions optimizer system 112 utilizes the modified gradient descentmodel 300 to determine whether to modify one or more of the constraints310 and/or target emissions values 312 in addition to any emissionsvalues modifications.

In one or more embodiments, the emissions optimizer system 112 utilizesa modified gradient descent model including a multi-variable objectivealgorithm such as a mixed-integer linear programming model toiteratively adjust emissions values for a plurality of physicalemissions sources. FIG. 4 illustrates a process by which a modifiedgradient descent model determines emissions value modifications forgenerating action recommendations to reduce emissions for an entity.Specifically, the emissions optimizer system 112 utilizes the modifiedgradient descent model to iteratively adjust emissions values forphysical emissions sources according to target emissions values and oneor more constraints.

As illustrated in FIG. 4 , the emissions optimizer system 112 determinesphysical emissions source data 400 for an entity. In particular, aspreviously mentioned, the physical emissions source data 400 includesinformation indicating unit numbers and source types of physicalemissions sources corresponding to the entity. For example, the physicalemissions source data 400 includes data representing one or moreprevious time periods (e.g., one or several years of recent data for theentity). In connection with determining the physical emissions sourcedata 400, the emissions optimizer system 112 also determines emissionsvalues based on the physical emissions source data 400.

According to one or more embodiments, the emissions optimizer system 112utilizes a modified gradient descent model 402 to process the physicalemissions source data 400. For example, the emissions optimizer system112 utilizes the modified gradient descent model 402 according to a setof unconstrained targets 404. Specifically, the emissions optimizersystem 112 provides the modified gradient descent model 402 with noconstraints to first determine whether the physical emissions sourcedata 400 or emissions values are erroneous or whether the modifiedgradient descent model or other component has an error. To illustrate,the modified gradient descent model 402 iterates through the emissionsvalues to determine if there is any combination of emissions values thatmeet the unconstrained targets 404. If the modified gradient descentmodel 402 does not output any results, the emissions optimizer system112 determines that there is an error 406 and returns to the physicalemissions source data 400 to find and correct the error with thephysical emissions source data 400, the corresponding emissions values,and/or the modified gradient descent model 402.

For instance, the modified gradient descent model 402 includes aniterative optimization algorithm that determines a local minimum of afunction given a number of variables. In particular, the modifiedgradient descent model 402 iteratively adjusts a set of initialparameter values to minimize a given cost function. In one or moreembodiments, the modified gradient descent model 402 finds the localminimum of a function by performing a plurality of steps proportional tothe negative of a gradient, which measures the change in weightsrelative to the change in error (e.g., a partial derivative with respectto a plurality of input variables). According to one or moreembodiments, in response to determining that the gradient reaches alocal minimum (e.g., the cost function is as small as possible), themodified gradient descent model 402 terminates. Furthermore, in one ormore embodiments, the modified gradient descent model 402 determines anumber of results according to the initial parameters and a learningrate. Thus, in some embodiments, the emissions optimizer system 112modifies the speed of the modified gradient descent model 402 byadjusting the number of input parameters and/or the learning rateassociated with the modified gradient descent model 402.

If the modified gradient descent model 402 outputs results, theemissions optimizer system 112 determines that the data/model are noterroneous moves to the next steps (i.e., optimizing the emissions valuesfor the entity). As illustrated, after determining that there is noerror in the physical emissions source data 400, the correspondingemissions values, and/or the modified gradient descent model 402, theemissions optimizer system 112 provides a set of constrained targets 408to the modified gradient descent model 402. In particular, the emissionsoptimizer system 112 utilizes entity-provided constraints and/orestimated constraints (e.g., via a machine-learning model) to optimizethe emissions values.

In one or more embodiments, the emissions optimizer system 112determines contribution proportions 410 corresponding to the pluralityof physical emissions sources to the emissions values. For instance, theemissions optimizer system 112 determines a total emissions value ofemissions produced by the physical emissions sources. In additionalembodiments, the emissions optimizer system 112 determines totalemissions values for a plurality of emission types produced by thephysical emissions sources. The emissions optimizer system 112determines percentage weights of the physical emissions sources (e.g., aweight for each source category) relative to the total emissions value(or to the total emissions value for each emission type). Accordingly,the emissions optimizer system 112 determines how much each physicalemissions source (or source category) contributes to the total emissionsproduced by the entity.

In one or more additional embodiments, the emissions optimizer system112 determines contributions of the physical emissions sources to one ormore additional parameters. For example, the emissions optimizer system112 determines contribution proportions of the physical emissionssources to total costs associated with the physical emissions sources(e.g., according to predefined cost values assigned based on a sourcecategory, emissions, or other data associated with a physical emissionssource). To illustrate, the emissions optimizer system 112 determinestotal costs associated with operations of objects and/or actionscorresponding to the physical emissions sources. The emissions optimizersystem 112 determines how much each of the physical emissions sources(or source categories) contributes to the total cost.

After determining the contribution proportions 410 of the physicalemissions sources to the total emissions value(s) and/or to one or moreadditional parameters, the emissions optimizer system 112 utilizes themodified gradient descent model 402 to optimize the emissions values forthe physical emissions sources based on the constrained targets 408.Specifically, the emissions optimizer system 112 utilizes the modifiedgradient descent model 402 to iteratively adjust emissions values forthe physical emissions sources according to the contribution proportions410. For instance, the emissions optimizer system 112 ranks/sorts thephysical emissions sources according to the contribution proportions410, such as by sorting the physical emissions sources from highestcontribution proportion to lowest contribution proportion.

The emissions optimizer system 112 utilizes the modified gradientdescent model 402 to adjust emissions values associated with thephysical emissions sources according to the contribution proportions410. To illustrate, the modified gradient descent model 402 selects thephysical emissions source with the highest contribution proportion andadjusts an emissions value of the selected physical emissions source.For example, the modified gradient descent model 402 determines a baseunit value for the selected physical emissions source indicating acurrent/most recent number of units of the physical emissions source.The modified gradient descent model 402 further determines a maximumnumber of units and a minimum number of units based on one or moreconstraints provided to the modified gradient descent model 402.

In one or more embodiments, the modified gradient descent model 402utilizes a search model (e.g., a binary search model) to select aninitial value corresponding to an emissions value modification 412 andstep the value up or down based on the generated results. With eachselected value, modified gradient descent model 402 determines whethercosts associated with the value provide optimal results 414 based on oneor more thresholds. To illustrate, the modified gradient descent model402 determines whether emissions values corresponding to the selectedvalue result in emissions values that are lower than a previousiteration. In additional embodiments, the modified gradient descentmodel 402 determines whether the emissions values corresponding to theselected value result in emissions values lower than a constraint (e.g.,an entity-defined emissions goal). In some embodiments, the modifiedgradient descent model 402 can also (or alternatively) determine whetherthe selected value lowers the overall emissions values while beinghigher than one or more constraints (e.g., a minimum unit number).

If the modified gradient descent model 402 generated results anddetermines that the selected value meets each of the above-indicatedthresholds, the emissions optimizer system 112 utilizes the modifiedgradient descent model 402 to iteratively determine one or more newvalues while performing the above process again. Specifically, themodified gradient descent model 402 utilizes the search model toiteratively select new values (e.g., by stepping up or down) anddetermine whether the new value meet the threshold(s). Once the modifiedgradient descent model 402 determines that a selected value providesresults that do not meet one or more of the above-indicated thresholds,the emissions optimizer system 112 may determine that the selected valuecorresponds to optimal results 414 for the emissions value modification412.

As illustrated in FIG. 4 , the emissions optimizer system 112 generatesan action recommendation 416 for providing to the entity to perform theemissions value modification 412. For example, the emissions optimizersystem 112 generates the action recommendation 416 including anindication to modify a number of units of a corresponding physicalemissions source. In some embodiments (e.g., as described with respectto FIG. 5 ), the emissions optimizer system 112 utilizes the emissionsvalues to generate the action recommendation 416 in a user-friendlyformat.

In one or more embodiments, the emissions optimizer system 112 utilizesthe modified gradient descent model 402 to continue optimizing theplurality of physical emissions sources until meeting the constrainedtargets 408. In particular, the emissions optimizer system 112determines, after optimizing a particular physical emissions source,whether the optimized emissions values meet the constrained targets 408.If not, the emissions optimizer system 112 utilizes the modifiedgradient descent model 402 to select another physical emissions source(e.g., the next highest contributing physical emissions source) andoptimize the newly selected physical emissions source. The emissionsoptimizer system 112 continues optimizing the physical emissions sourcesand generating action recommendations for emissions value modificationsuntil meeting the constrained targets 408.

In some embodiments, if the emissions optimizer system 112 iteratesthrough all physical emissions sources and does not meet the constrainedtargets 408, the emissions optimizer system 112 determines that theconstraints and/or the target emissions values are unrealistic (i.e.,not possible given the physical emissions sources). Accordingly, in oneor more embodiments, the emissions optimizer system 112 utilizes themodified gradient descent model 402 to adjust the emissions values ofthe physical emissions sources with only the constraints (e.g., with nouser-defined target emissions values). If the modified gradient descentmodel 402 generates valid results, the emissions optimizer system 112repeats the optimization process for the physical emissions sources tooptimize the emissions values as much as possible toward a set ofmodel-defined target emissions values (e.g., default target emissionsvalues).

If the modified gradient descent model 402 does not generate validresults, the emissions optimizer system 112 determines that one or moreof the constraints are not possible. According to one or moreembodiments, the emissions optimizer system 112 relaxes one or moreconstraints to determine modified constraints 418 (e.g., byincrementally reducing or increasing specific constraint values) andutilizes the modified gradient descent model 402 to optimize theresults, if possible. The emissions optimizer system 112 provides one ormore action recommendations in connection with the modified constraints418. For instance, the emissions optimizer system 112 generates one ormore action recommendations to modify one or more physical emissionssources and one or more action recommendations based on the modifiedconstraints 418 for use in determining the constrained targets 408.Furthermore, if the emissions optimizer system 112 determines that themodified gradient descent model 402 is unable to produce valid resultswith the modified constraints 418, the emissions optimizer system 112modifies the target emissions values and repeats the process untildetermining target emissions values that produce valid results.

As mentioned, in one or more embodiments, the emissions optimizer system112 generates action recommendations in a user-friendly format. FIG. 5illustrates a diagram of the emissions optimizer system 112 generatingnatural language recommendations. In particular, the emissions optimizersystem 112 utilizes a natural language processing engine 500 to convertor transform emissions values modifications 502 to a plurality ofnatural language action recommendations 504 a-504 n. More specifically,the emissions optimizer system 112 utilizes the natural languageprocessing engine 500 to convert data associated with the emissionsvalues modifications 502 into the natural language actionrecommendations 504 a-504 n.

In one or more embodiments, the emissions optimizer system 112 utilizesthe natural language processing engine 500 to process the emissionsvalues modifications 502. For example, the emissions optimizer system112 determines physical emissions source data and an emissions valuesmodification for a physical emissions source. The emissions optimizersystem 112 utilizes the natural language processing engine 500 togenerate one or more natural language phrases or sentences that describethe physical emissions source data and the emissions valuesmodification.

In one or more embodiments, the natural language processing engine 500includes a neural network that converts structured data into naturallanguage phrases. To illustrate, the natural language processing engine500 includes a language-based neural network such as a generativetransformer-based neural network or a long short-term memory neuralnetwork to extract relationships between data points and convert theextracted relationships into natural language phrases referencing thedata points. The natural language processing engine 500 converts thephysical emissions source data and emissions value modifications togenerate natural language phrases indicating one or more actions toachieve a desired result.

For example, the natural language processing engine 500 determinesrelationships between values in physical emissions source data. In oneor more embodiments, the natural language processing engine 500 alsodetermines relationships between initial physical emissions source dataand modified physical emissions source data (e.g., based on differencesbetween initial emissions values and modified emissions values). Thenatural language processing engine 500 converts the relationships tonatural language phrases by generating sentences or phrases indicatingthe relationships or differences.

As discussed above, in one or more embodiments, the emissions optimizersystem 112 utilizes a deep-learning based natural language processingmodel (e.g., an NLP model) to determine intent classificationsassociated with instances of natural language input. For instance, theemissions optimizer system 112 utilizes a natural language processingengine 500 or NLP model including an encoder layer and a decoder layer.

As mentioned above, the encoder layer receives a structured data input(e.g., the emissions values modifications) and parses the input intowords, characters, or character n-grams. In one or more embodiments, theemissions optimizer system 112 embeds the words, characters, orcharacter n-grams into one or more input vectors. For example, theemissions optimizer system 112 can encode the input utilizing one-hotencoding, or a neural embedding based on word semantics.

In one or more embodiments, the emissions optimizer system 112 feeds thegenerated input vector for each word in the input to the encoder layerincluding bi-directional LSTM layers. The bi-directional LSTM layers ofthe encoder layer can each include a first layers and second layers. Inat least one embodiment, the first and second layers include series ofLSTM units that are organized bi-directionally. In one or moreembodiments, the bi-directional organization divides the LSTM units intotwo directions. For example, half of the LSTM units are organized‘forward,’ or in a sequence over increasing sequence instances, whilethe other half of the LSTM units are organized ‘backward,’ or in asequence over decreasing sequence instances. By organizing the LSTMunits in opposite directions, the encoder layer can simultaneouslyutilize content information from the past and future of the currentsequence instance to inform the output of the encoder layer.

Generally, each LSTM unit includes a cell, an input gate, an outputgate, and a forget gate. As such, each LSTM unit can “remember” valuesover arbitrary time intervals while regulating the flow of informationinto and out of the unit. Thus, for example, a first LSTM unit in thefirst layer of the encoder layer can analyze an input vector encodingthe a first input token. A second LSTM unit in the first layer cananalyze an input vector encoding a second input token as well as afeature vector from the first LSTM unit (e.g., a latent feature vectorencoding significant features of the first input or other previousinputs in the sequence).

The natural language processing engine 500 sequentially models theinput, where latent feature vectors of previous layers (corresponding toprevious text inputs and training text inputs) are passed to subsequentlayers, and where hidden states of text inputs are obtained to generatevectors for each word embedded into the input vector. Each of the layersof the encoder layer further determine relationships between wordsembedded into the input vector and other contextual information togenerate output vectors.

For example, the encoder layer can output a sequence vector that feedsdirectly into the decoder layer. The decoder layer is configuredsimilarly to the encoder layer with multiple bi-directional LSTM layers.In response to receiving the sequence vector from the encoder layer, thelayers of the decoder layer can output a predicted phrase or sentenceindicating one or more actions to achieve a desired result based on thephysical emissions source data and emissions value modifications.

To illustrate, the emissions optimizer system 112 determines that thephysical emissions source data indicates a number of units and/oremissions values for a physical emissions source or an emission type fora previous year and an emissions values modification that indicates anew number of units and/or emissions values for a future time period.The emissions optimizer system 112 utilizes the natural languageprocessing engine 500 to generate a sentence indicating the change invalues from the previous time period to the future time period. As anexample, the resulting natural language recommendation includes “Reducenatural gas from 15 K in the base year (2020) to 13 K in the target year(2022).” In an additional example, the emissions optimizer system 112also provides natural language action recommendations in connection withspecific business actions such as “Increase electric vehicles from 14 inthe base year (2020) to 18 in the target year (2022).” In additionalembodiments, the emissions optimizer system 112 also generates naturallanguage recommendations including budgetary implications of emissionsvalues modifications.

In one or more embodiments, the emissions optimizer system 112 utilizesuser inputs to further train the natural language processing engine 500.To illustrate, the emissions optimizer system 112 utilizes a selectednatural language action recommendation to further train the naturallanguage processing engine 500 for future recommendations (e.g., as apositive example to steer the natural language processing engine 500 toproduce similar recommendations/styles in the future). Additionally, theemissions optimizer system 112 utilizes the unselected recommendationsas negative examples for training the natural language processing engine500.

As previously described, in one or more embodiments, the emissionsoptimizer system 112 determines constraints for determining emissionsvalues modifications. For example, the emissions optimizer system 112receives user-defined constraints and/or target emissions values for anentity. FIGS. 6A-6B illustrate graphical user interfaces for settingconstraints and target emissions values. Specifically, FIG. 6Aillustrates a graphical user interface for setting a plurality ofconstraints for a plurality of physical emissions sources. FIG. 6Billustrates a graphical user interface for setting one or more targetemissions values for specific emission types.

FIG. 6A illustrates a client device 600 presenting a graphical userinterface of a client application 602 for various entity managementoperations. Specifically, the client device 600 displays a plurality ofgraphical user interface elements 604 corresponding to a plurality ofdifferent physical emissions sources. For instance, the client device600 displays a graphical user interface element 604 a corresponding to afirst physical emissions source (e.g., a “Restaurant Heater”) for anentity. In connection with the graphical user interface element 604 a,the client device 600 receives user input to define one or moreconstraints for the first physical emissions source.

To illustrate, the client device 600 displays a minimum constraint 606 aindicating a minimum number of units, minimum costs, or other minimumvalue associated with the first physical emissions source. The clientdevice 600 also displays a maximum constraint 606 b indicating a maximumnumber of units, maximum costs, or other maximum value associated withthe first physical emissions source. Accordingly, the emissionsoptimizer system 112 determines various constraints associated with thephysical emissions sources based on user inputs via the client device600.

As illustrated in FIG. 6A, in one or more embodiments, the client device600 also displays results generated by the emissions optimizer system112. In particular, the emissions optimizer system 112 utilizes amodified gradient descent model to generate a plurality of emissionsvalues modifications based on the provided constraints. For instance,the emissions optimizer system 112 generates an emissions valuemodification for the first physical emissions source based on theminimum constraint 606 a and the maximum constraint 606 b. The emissionsoptimizer system 112 provides the emissions value modification to theclient device 600, which displays a modification element 608 indicatingthe emissions value modification. As shown, the emissions optimizersystem 112 generated the emissions value modification to include a valuebetween the minimum constraint 606 a and the maximum constraint 606 b,which provides an easily verifiable, user friendly format for viewingmodifications to perform relative to the physical emissions sources.

FIG. 6B illustrates the client device 600 presenting an additionalgraphical user interface of the client application 602. In particular,the client device 600 displays a plurality of graphical user interfaceelements 610 corresponding to a plurality of different emission types.For instance, the client device 600 displays a graphical user interfaceelement 610 a corresponding to a total emissions representing acombination of all emission produced by physical emissions sources forthe entity. In connection with the graphical user interface element 610a, the client device 600 receives user input to define one or moreconstraints for the total emissions.

To illustrate, the client device 600 displays a minimum constraint 612 aindicating a minimum total emissions value for emissions produced byphysical emissions sources for the entity. The client device 600 alsodisplays a maximum constraint 612 b indicating a maximum total emissionsvalue for emissions produced by physical emissions sources for theentity. In some embodiments, the client device 600 also receives userinputs for setting one or more constraints associated with one or moreof the different emission types (minimum/maximum emissions values for afirst emission type, minimum/maximum emissions values a second emissiontype, etc.) The emissions optimizer system 112 thus determines variousconstraints associated with the emissions produced by the physicalemissions sources for the entity based on one or more user inputs viathe client device 600.

As illustrated in FIG. 6B, in some embodiments, the client device 600also displays results generated by the emissions optimizer system 112.In particular, the emissions optimizer system 112 utilizes a modifiedgradient descent model to generate a target total emissions value. Forinstance, the emissions optimizer system 112 utilizes the modifiedgradient descent model to iteratively adjust emissions values for aplurality of physical emissions sources to determine one or moreemissions values modifications based on provided constraints (e.g.,based on constraints for the physical emissions sources and theemissions values). The emissions optimizer system 112 provides aresulting total emissions value to the client device 600, which displaysa total emissions element 614 indicating the resulting total emissionsbased on one or more emissions value modifications. Additionally, in oneor more embodiments, the client device 600 displays emission typeelements (e.g., a first emission type element 616) indicating resultsgenerated for individual emission types in connection with the resultingtotal emissions value.

In one or more additional embodiments, the emissions optimizer system112 provides additional methods for users to indicate constraints and/ortarget emissions values. For instance, rather than the graphical userinterface elements of FIGS. 6A-6B, the emissions optimizer system 112can provide slider elements, text fields, or other graphical userinterface elements. In additional embodiments, the emissions optimizersystem 112 utilizes one or more machine-learning models to determine oneor more constraints for an entity. Furthermore, after determining one ormore predicted constraints utilizing machine-learning models, theemissions optimizer system 112 can also provide the predictedconstraints to a client device for confirmation and/or adjustment by auser associated with the entity.

FIG. 7 illustrates a graph diagram 700 of a plurality of resultsgenerated via a plurality of iterations of a modified gradient descentmodel in connection with a plurality of physical emissions sources, aplurality of constraints, and one or more target emissions values. Inone or more embodiments, as mentioned, the emissions optimizer system112 utilizes the modified gradient descent model to iteratively adjustemissions values for a plurality of physical emissions sources accordingto the constraints and the target emissions value(s). Specifically, thegraph diagram 700 represents solutions generated by the modifiedgradient descent model of emissions (e.g., emissions values) for aplurality of physical emissions sources relative to source costs (e.g.,according to predefined emission cost per unit of physical emissionssource).

For example, as illustrated in FIG. 7 , the emissions optimizer system112 determines maximum user constraints 702 a and minimum userconstraints 702 b indicating maximum and minimum values, respectively,of source costs. After determining the constraints, the emissionsoptimizer system 112 utilizes the modified gradient descent model toiteratively adjust emissions values of a plurality of physical emissionssources toward one or more target emissions values. As the modifiedgradient descent model adjusts the emissions values, the emissionsoptimizer system 112 also determines whether the resulting emissionvalues meet the maximum user constraints 702 a and the minimum userconstraints 702 b.

To illustrate, the emissions optimizer system 112 determines thatresults above the maximum user constraints 702 a or below the minimumuser constraints 702 b are infeasible solutions. Furthermore, theemissions optimizer system 112 determines that results that meet themaximum user constraints 702 a and the minimum user constraints 702 bare feasible solutions. The emissions optimizer system 112 utilizes themodified gradient descent model to iteratively adjusts the emissionsvalues until determining one or more optimal results. Specifically, asillustrated in FIG. 7 , the emissions optimizer system 112 determinesfeasible solutions that meet the constraints but do not meet one or moretarget emissions values. Accordingly, the emissions optimizer system 112continues adjusting emissions values to determine optimal results 704including feasible solutions that meet the constraints and also meet theone or more target emissions values.

As illustrated in FIG. 7 , the emissions optimizer system 112 is able toquickly and efficiently determine feasible solutions that meet a set ofconstraints and target emissions values by adjusting emissions valuesfor individual physical emissions sources in an iterative process. Asshown, the solution space can include a very large number of possiblesolutions (e.g., hundreds or thousands or more) depending on the numberof physical emissions sources and constraints. By utilizing the modifiedgradient descent model with an efficient search model, the emissionsoptimizer system 112 reduces the number of solutions generated to asmall fraction of the total possible solutions. Accordingly, theemissions optimizer system 112 significantly improves the efficiency ofa computing device by reducing the computing resources required togenerate results that reduce emissions for a plurality of physicalemissions sources of the entity.

According to one or more embodiments, the emissions optimizer system 112determines a plurality of results that meet constraints and also meettarget emissions values. For example, the emissions optimizer system 112determines a plurality of different combinations of emissions valuemodifications for a plurality of physical emissions sources that eachmeets the constraints and target emissions values. To illustrate, theemissions optimizer system 112 utilizes the modified gradient descentmodel to generate a plurality of different results by processing thephysical emissions sources according to different criteria (e.g., basedon contribution proportions relative to emissions values, contributionproportions relative to source costs, or other sorting methods). Theemissions optimizer system 112 provides action recommendations for eachresult in the optimal results 704.

FIGS. 8A-8F illustrate chart diagrams of an example in which theemissions optimizer system 112 utilizes a modified gradient descentmodel to generate emissions value modifications for a plurality ofphysical emissions sources. For example, FIG. 8A illustrates a chartdiagram 800 including a plurality of physical emissions sourcescorresponding to an entity. In one or more embodiments, the physicalemissions sources correspond to specific source categories (e.g., “fueltype,” “mobile combustion, ” “transport”). Additionally, the chartdiagram 800 includes entity usage including heaters, ovens, buildings,travel, etc., along with the emissions sources such as gas, electricity,fuel, etc. Furthermore, as illustrated in FIG. 8A, the emissionsoptimizer system 112 determines unit costs, unit sizes, and unit (e.g.,“mmBTU,” “gal,” “vehicle-mile”) for each physical emissions source.

In one or more embodiments, as illustrated in FIG. 8A, the emissionsoptimizer system 112 also determines physical emissions source data forone or more previous time periods. Specifically, the emissions optimizersystem 112 determines a number of units for each emissions source for aprevious time period (e.g., the most recent year) corresponding to theentity. For example, the emissions optimizer system 112 accesses adatabase or repository including information about the number of unitsof the plurality of physical emissions sources for the entity. Thephysical emissions source data allows the emissions optimizer system 112to determine emissions, costs, etc., resulting from the plurality ofphysical emissions sources for the entity.

To illustrate, FIG. 8B illustrates a chart diagram 802 of a plurality ofemission types produced by the physical emissions sources for theentity. For instance, the emissions optimizer system 112 accesses adatabase including emissions data to determine emissions values of aplurality of emission types produced by each physical emissions source.To illustrate, the database includes the amount of emissions generatedby a single unit of each physical emissions source. As shown in FIG. 8B,each unit of physical emissions source produces different emission typesbased on the source category of the physical emissions source. In someembodiments, the emissions optimizer system 112 determines specificemission types based on the entity, such as based on a region of theentity, a size of the entity, or other attributes of the entity,according to local regulations and/or goals of the entity.

FIG. 8C illustrates a chart diagram 804 including constraints for amodified gradient descent model and results generated by the modifiedgradient descent model based on the constraints and further based on thephysical emissions sources of FIG. 8A. In one or more embodiments, theemissions optimizer system 112 determines minimum and maximum sourcevalues (e.g., numbers of units of corresponding physical emissionssources). The emissions optimizer system 112 utilizes the modifiedgradient descent model to generate emissions value modifications byiteratively adjusting the emissions values for the physical emissionssources (e.g., by adjusting the number of units for the physicalemissions sources up or down). The chart diagram 804 includes theresults from the modified gradient descent model (“Optimizer Output”)indicating that the modified gradient descent model produced resultswithin the provided constraints.

FIG. 8D illustrates a chart diagram 806 including comparisons ofemissions values and source costs between the base year (2020) and theresults of the modified gradient descent model for the entity for afuture time period (e.g., 2022). Specifically, the chart diagram 806includes source costs of the base year according to the number of unitsof the plurality of physical emissions sources and the source costsassociated with the physical emissions sources. In addition, the chartdiagram 806 includes the contribution proportions of the physicalemissions sources as percentages of the total source costs and the totalemissions values for the base year and the modified gradient descentmodel results. As illustrated, the emission source “CNG - Light-dutyvehicles” includes the highest contribution to the total source costs,while the emissions source “Blast Furnace Gas” corresponding to therestaurant heater produces the highest contribution to the totalemissions. Furthermore, as illustrated in the chart diagram 806, theresults of the modified gradient descent model include higher sourcecosts relative to the source costs of the base year.

While the modified gradient descent model generated results with highercosts for the future time period, the chart diagram 806 also indicatesthat the modified gradient descent model produced results that reduceemissions for the future time period. Specifically, as illustrated inFIG. 8D, the emissions optimizer system 112 utilizes the modifiedgradient descent model to adjust emissions values for some of thephysical emissions sources relative to the base year. For example, theemissions optimizer system 112 generates emissions value modificationsthat result in a decrease of total emissions values from 28,133.14 to26,534.17-a total decrease of 5.6%.

FIG. 8E illustrates a chart diagram 808 including the total emissionsvalues for the plurality of physical emissions sources based on thenumber of units of each physical emissions source for the base year.Additionally, the chart diagram 808 includes the total emissions valuesof each emission type contributing to the total emissions values. FIG.8F illustrates a chart diagram 810 including the total emissions valuesfor the plurality of physical emissions sources based on the number ofunits of each physical emissions source for the future time period(e.g., results generated by the modified gradient descent model). Thechart diagram 810 includes the total emissions values of each emissiontype contributing to the total emissions values. As shown in FIGS.8E-8F, the emissions optimizer system 112 provides improved emissionsreductions across the plurality of emission types by adjusting emissionsvalues for the plurality of physical emissions sources in accordancewith the constraints and one or more target emissions values.

As mentioned, the emissions forecasting system 102 utilizes a pluralityof forecasting machine-learning models to forecast emissions data for aplurality of physical emissions sources for a future time period. FIG. 9illustrates an overview of the emissions forecasting system 102processing physical emissions source data 900 of physical emissionssources to generate action recommendations 902. In particular, theemissions forecasting system 102 utilizes the plurality of forecastingmachine-learning models 116 to generate forecasted emissions valuemodifications for the physical emissions sources for one or more futuretime periods (e.g., an upcoming year or years). The emissionsforecasting system 102 generates the action recommendations 902 based onthe forecasted emissions value modifications. Additionally, FIG. 9illustrates the emissions monitoring system 103 providing alerts 904based on deviations of emissions data during the one or more future timeperiods from forecasted data.

According to one or more embodiments, the emissions forecasting system102 determines the physical emissions source data 900 in connection witha plurality of physical emissions sources for an entity. To illustrate,as previously described, the emissions forecasting system 102 determinesa number and a type of each of a plurality of physical emissions sourcescorresponding to the entity. In additional embodiments, the physicalemissions source data 900 includes additional source attributescorresponding to the physical emissions sources such as, but not limitedto, emissions costs or other costs, source categories, emission types,or other attributes of the physical emissions sources.

In one or more embodiments, the emissions forecasting system 102determines the forecasting machine-learning models 116 for the physicalemissions sources based on attributes of the physical emissions sources.For instance, a machine-learning model of the forecastingmachine-learning models 116 include a computer representation that istuned (e.g., trained) based on inputs to approximate unknown functions.For instance, a machine-learning model includes a neural network withone or more layers or artificial neurons that approximate unknownfunctions by analyzing known data at different levels of abstraction. Insome embodiments, a machine-learning model includes one or more neuralnetwork layers including, but not limited to, a deep learning model, aconvolutional neural network, a recurrent neural network, afully-connected neural network, or a combination of a plurality ofneural networks and/or neural network types. In additional embodiments,a machine-learning model includes, but is not limited to, anautoregressive moving average model, a seasonal autoregressiveintegrated moving average model, an ensemble learning model, a linearregression model, or a weighted model. Accordingly, in come embodiments,the forecasting machine-learning models 116 include a variety ofdifferent machine-learning models based on the corresponding physicalemissions sources.

According to one or more embodiments, the emissions forecasting system102 utilizes the forecasting machine-learning models 116 to generate theaction recommendations 902 based on the physical emissions source data900. Specifically, the emissions forecasting system 102 utilizes theforecasting machine-learning models 116 to generate forecasted sourceattributes for the physical emissions sources for a future time period.The emissions forecasting system 102 utilizes the forecasted sourceattributes to determine forecasted emissions value modifications for thefuture time period. Furthermore, the emissions forecasting system 102generates the action recommendations 902 based on the forecastedemissions value modifications. For example, as described in more detailbelow with respect to FIG. 10 and FIGS. 11A-11C, the emissionsforecasting system 102 generates the action recommendations 902 based onforecasted emissions data.

Additionally, the emissions monitoring system 103 monitors the physicalemissions source data 900 in connection with the forecasted datagenerated by the emissions forecasting system 102 to determine theentity’s performance relative to a generated emissions plan for a futuretime period. In particular, as described in more detail below withrespect to FIGS. 10 and 11C, the emissions monitoring system 103 detectsdeviations from a forecasted emissions plan based on an entity’semissions usage during a future time period. The emissions monitoringsystem 103 also generates alerts 904 to provide to the entity forcorrecting the deviation according to the emissions plan.

FIG. 10 illustrates a diagram of a detailed process of the emissionsforecasting system 102 generating action recommendations 1000 formodifying physical emissions sources corresponding to an entity based ona plurality of forecasted source attributes. In particular, theemissions forecasting system 102 utilizes a plurality of forecastingmachine-learning models 116 to generate forecasted emissions data for afuture time period. The emissions forecasting system 102 utilizes theforecasted emissions data to generate the action recommendations 1000.In some embodiments, as illustrated in FIG. 10 , the emissionsforecasting system 102 utilizes optimized emissions data generated bythe emissions optimizer system 112 for the entity to generate theforecasted emissions data. In alternative embodiments, the emissionsforecasting system 102 utilizes entity data (e.g., constraints, goals,or other information associated with the entity) without optimizedemissions data to generate the forecasted emissions data.

As illustrated in FIG. 10 , in one or more embodiments, the emissionsoptimizer system 112 utilizes a modified gradient descent model 1002 togenerate optimized emissions data for an entity according to dataassociated with the entity-including goals and limitations orconstraints for the entity. To illustrate, the emissions optimizersystem 112 utilizes the modified gradient descent model 1002 to processphysical emissions source data 1004 according to constraints 1006 and aset of target emissions values 1008. The emissions optimizer system 112utilizes the provided data to determine emissions value modifications1010 for modifying the physical emissions sources corresponding to theentity. By processing the physical emissions source data 1004 accordingto constraints 1006 and a set of target emissions values 1008 (e.g., viathe modified gradient descent model 1002), the emissions optimizersystem 112 generates an initial set of action recommendations accordingto the goals/limitations of the entity.

According to one or more embodiments, the emissions optimizer system 112determines historical data 1004 a associated with the physical emissionssource of the entity including attributes that determine an impact of aplurality of physical emissions sources on one or more defined goals forthe entity such as those described with respect to FIG. 3 above. Toillustrate, the historical data 1004 a includes, but is not limited to,a number of units of each physical emissions source, source categoriesof the physical emissions sources, emissions values for the plurality ofphysical emissions sources based on an emissions protocol, monetary (orother) costs associated with the plurality of physical emissionssources, or types of emissions produced by physical emissions sources.In some embodiments, the historical data 1004 a also includes data forindividual physical emissions sources, such that the emissionsforecasting system 102 is able to determine the attributes of eachseparate physical emissions source.

In one or more embodiments, the emissions optimizer system 112determines constraints 1006 in connection with determining whether tomodify physical emissions sources. For example, as previously indicated,the constraints 1006 include indications of requirements or limitationssuch as source constraints, budget constraints, or additionalconstraints. In at least some embodiments, the constraints 1006 includebudget constraints for individual physical emissions sources or sourcecategories or for overall financial expenditures related to the physicalemissions sources. In some embodiments, the constraints 1006 include anindication of a growth level (e.g., moderate or aggressive growthincluding increases in parameters for various physical emissionssources) for the entity, one or more physical emissions sources, or theforecasted values.

According to one or more embodiments, the emissions optimizer system 112determines target emissions values 1008 for the entity. For instance,the emissions optimizer system 112 determines a target emissions valuefor total emissions produced in connection with the physical emissionssources for optimizing the physical emissions sources and emissionsoutput for the entity. In additional embodiments, the emissionsoptimizer system 112 determines target emissions values for individualphysical emissions sources or source categories. Thus, the emissionsoptimizer system 112 determines the target emissions values 1008 inconnection with an emissions goal for the entity.

After determining the physical emissions source data 1004, theconstraints 1006, and the target emissions values 1008, the emissionsoptimizer system 112 generates emissions value modifications 1010. Inparticular, the emissions optimizer system 112 utilizes the modifiedgradient descent model 1002 to generate the emissions valuemodifications 1010. For example, as previously described, the emissionsoptimizer system 112 utilizes the modified gradient descent model 1002to iteratively adjust emissions values associated with the physicalemissions sources to determine how to modify the physical emissionssources (e.g., by modifying the corresponding emissions values) toachieve the target emissions values 1008 given the constraints 1006 andphysical emissions source data 1004.

In one or more embodiments, in connection with the emissions optimizersystem 112 utilizing the modified gradient descent model 1002 toevaluate the physical emissions source data 1004 based on theconstraints 1006 and target emissions values 1008 for an entity, theemissions forecasting system 102 generates forecasted data for one ormore future time periods for the entity. Specifically, as illustrated inFIG. 10 , the emissions forecasting system 102 utilizes the physicalemissions source data 1004 (e.g., the historical data 1004 a) and theconstraints 1006 to generate forecasted emissions value modifications1014. More specifically, the emissions forecasting system 102 utilizesthe forecasting machine-learning models 116 to generate the forecastedemissions value modifications 1014 indicating forecasted emissionsproduction for a plurality of physical emissions sources based on thehistorical data 1004 a associated with the physical emissions sourcesand the constraints 1006 for the entity.

In one or more embodiments, the emissions forecasting system 102utilizes the forecasting machine-learning models 116 to generate variousforecasted source attributes for the plurality of physical emissionssources. For instance, the emissions forecasting system 102 utilizes theforecasting machine-learning models 116 to forecast specific attributesof each physical emissions source for the entity based on the historicalsource attributes and constraints/goals for the entity. In someembodiments, the forecasted source attributes include, but are notlimited to, unit numbers and per-unit costs for the physical emissionssources. Accordingly, the emissions forecasting system 102 generatespredictions of the unit numbers and per-unit costs for the physicalemissions sources for one or more future time periods. The emissionsforecasting system 102 generates the forecasted emissions valuemodifications 1014 based on the forecasted source attributes.

As shown in FIG. 10 , in some embodiments, the emissions forecastingsystem 102 also generates the forecasted emissions value modifications1014 based on optimized results generated by the emissions optimizersystem 112. In particular, the emissions forecasting system 102 utilizesthe emissions value modifications 1010 generated utilizing the modifiedgradient descent model 1002 in connection with the forecastingmachine-learning models 116 (e.g., under an assumption that the entitywill use the emissions optimizer system 112 to optimize an emissionsplan). Accordingly, the emissions forecasting system 102 generates theforecasted emissions value modifications 1014 based on the emissionsvalue modifications 1010, the physical emissions source data 1004, andthe constraints 1006. In alternative embodiments, the emissionsforecasting system 102 generates the forecasted emissions valuemodifications 1014 based on the physical emissions source data 1004 andconstraints 1006 without initial optimization via the emissionsoptimizer system 112.

In one or more embodiments, the emissions forecasting system 102generates the action recommendations 1000 based on the forecastedemissions value modifications 1014. Specifically, the emissionsforecasting system 102 generates the action recommendations 1000 toperform one or more actions in relation to the physical emissionssources for one or more future time periods. To illustrate, theemissions forecasting system 102 generates an action recommendation toperform one or more actions including, but not limited to, modifyingphysical emissions sources, modifying one or more constraints, ormodifying one or more target emissions values or goals for a future timeperiod. The emissions forecasting system 102 can also provide aplurality of action recommendations for a plurality of different futuretime periods or combinations of future time periods related toforecasted emissions data according to different predicted sourceattributes for the physical emissions sources corresponding to theentity. Thus, the action recommendations 1000 provide actions to performbased on likely scenarios for emissions costs (or other sourceattributes) in connection with past and future growth plans for theentity.

In one or more embodiments, the emissions monitoring system 103 utilizesforecasted data generated by the emissions monitoring system 103 tonotify an entity of deviations from an emissions plan. For example, inresponse to detecting a deviation from a forecasted emissions value forone or more physical emissions sources (e.g., based on data from theemissions optimizer system 112 and/or the emissions forecasting system102), the emissions monitoring system 103 determines whether thedeviation meets a threshold value. Based on the deviation meeting thethreshold value, the emissions monitoring system 103 generates an alert1016 including one or more action recommendations to provide to theentity for correcting the deviation with respect to the one or morephysical emissions sources. To illustrate, the emissions monitoringsystem 103 communicates with the emissions optimizer system 112 and/orthe emissions forecasting system 102 to generate updated forecasts basedon the deviation. Accordingly, even when the entity fails to achieve aparticular forecasted emissions value for a future time period, theemissions monitoring system 103 provides real-time monitoring andupdated recommendations to adjust emissions usage to still meet theforecasted goals.

Although FIG. 10 illustrates the emissions optimizer system 112, theemissions forecasting system 102, and the emissions monitoring system103 as being separate, in alternative embodiments, the emissionsoptimizer system 112, the emissions forecasting system 102, and theemissions monitoring system 103 are part of a single component. Forexample, the emissions optimizer system 112 may include the emissionsforecasting system 102 and/or the emissions monitoring system 103, orthe emissions forecasting system 102 may include the emissions optimizersystem 112 and the emissions monitoring system 103, etc. Additionally,the emissions optimizer system 112 may include the forecastingmachine-learning models 116 and/or the emissions forecasting system 102may include the modified gradient descent model 1002.

FIGS. 11A-11C illustrate diagrams of the emissions forecasting system102 generating action recommendations based on forecasted emissions datafor physical emissions sources corresponding to an entity. Specifically,as illustrated in FIG. 11A, the emissions forecasting system 102generates forecasted emissions data for a plurality of physicalemissions sources 1100 a-1100 n. For instance, the emissions forecastingsystem 102 utilizes a plurality of forecasting machine-learning models1102-1102 n to process data associated with the physical emissionssources 1100 a-1100 n and generate forecasted source attributes 1104a-1104 n for the physical emissions sources 1100 a-1100 n for a futuretime period.

As previously mentioned, in one or more embodiments, the physicalemissions sources 1100 a-1100 n are associated with historical dataindicating emissions data or other source attributes of the physicalemissions sources 1100 a-1100 n during one or more previous timeperiods. For example, the emissions forecasting system 102 determineshistorical data from a most recent time period (e.g., the last year orthe last 3-6 months). In another example, the emissions forecastingsystem 102 determines historical data most representative of aforecasting time period. To illustrate, the emissions forecasting system102 determines historical data from a time period with similarseasonality attributes (e.g., seasonal demand) to the forecasted timeperiod (e.g., to forecast source attributes for the future time periodbased on the most relevant historical data).

According to one or more embodiments, the emissions forecasting system102 determines the forecasting machine-learning models 1102-1102 n basedon the physical emissions sources 1100 a-1100 n. In particular, theemissions forecasting system 102 determines a forecastingmachine-learning model for a physical emissions source based on one ormore attributes of the historical data associated with the physicalemissions source. To illustrate, the emissions forecasting system 102determines a first forecasting machine-learning model 1102 a for a firstphysical emissions source 1100 a according to the availability (e.g.,amount) of historical data and/or type (e.g., the type(s) of sourceattributes) of historical data for the first physical emissions source1100 a. Furthermore, the emissions forecasting system 102 determines asecond forecasting machine-learning model 1102 b for a second physicalemissions source 1100 b according to the availability and/or type ofhistorical data for the second physical emissions source 1100 b. Theemissions forecasting system 102 thus determines separate forecastingmachine-learning models for the physical emissions sources 1100 a-1100 naccording to the nature of the available data for the physical emissionssources 1100 a-1100 n. In some embodiments, emissions forecasting system102 also uses one or more statistical or heuristic models for one ormore physical emissions sources according to the available data for thephysical emissions sources.

In one or more embodiments, the emissions forecasting system 102determines one or more attributes of a given dataset (e.g., historicaldata) associated with a physical emissions source 1100 a-1100 n. Forexample, in one or more embodiments, the emissions forecasting system102 analyses a dataset to determine one or more of the linearity, thestationarity, the volatility, or the size of the dataset. Specifically,in one or more embodiments, the emissions forecasting system 102performs time series decomposition to determine attributes of a dataset.The emissions forecasting system 102 selects a forecastingmachine-learning model based on the one or more determined attributes ofthe dataset.

More specifically, the emissions forecasting system 102 can determinethe linearity of a dataset. For example, the emissions forecastingsystem 102 determines whether the data of a dataset has a linear ornon-linear relationship. For example, the emissions forecasting system102 generates a data visualization of a time series (e.g., a scatterplot) to determine if point cluster forms a diagonal line.

Additionally, the emissions forecasting system 102 can determine thestationarity of a dataset. For instance, the emissions forecastingsystem 102 determines whether the dataset is stationary ornon-stationary. The emissions forecasting system 102 determines that adataset is stationary when the time series of the dataset has no trend(e.g., a long-term upward or downward pattern) or seasonality (e.g., aperiodic fluctuation). The emissions forecasting system 102 determinesthat a dataset is non-stationary when the time series of the dataset hasa trend or seasonality. In one or more implementations, the emissionsforecasting system 102 utilizes a Dickey-Fuller or an AugmentedDickey-Fuller (ADF) test to determine data stationarity.

The emissions forecasting system 102 can determine the volatility of adataset. For example, the emissions forecasting system 102 determineswhether a time series is volatile or non-volatile. The emissionsforecasting system 102 determines that a time series is volatile if thetime series includes unexpected rises or falls. In one or moreimplementations, the emissions forecasting system 102 utilizes aLagrange Multiplier (LM) test to assess the volatility of a dataset. Inparticular, the emissions forecasting system 102 utilizes the p-valuesof the ADF and LM tests to indicate if the null hypotheses of the testare accepted to determine the stationarity and volatility of the data.In addition, the emissions forecasting system 102 determines a datasetsize based on the number of observations in the dataset.

As mentioned, the emissions forecasting system 102 selects a forecastingmachine-learning model based on the one or more determined attributes ofthe dataset. For example, based on determining that a dataset is linear,stationary, and non-volatile, the emissions forecasting system 102selects a regression model as a forecasting machine-learning model. Inparticular, based on determining that a dataset is linear, stationary,and non-volatile, the emissions forecasting system 102 selects anautoregressive integrated moving average model as a forecastingmachine-learning model. In one or more further implementations, based ondetermining that a dataset is non-linear, non-stationary, andnon-volatile, the emissions forecasting system 102 selects a supportvector machine as a forecasting machine-learning model. In one or morefurther implementations, based on determining that a dataset isnon-linear, non-stationary, and volatile, the emissions forecastingsystem 102 selects a random forest model as a forecastingmachine-learning model.

In one or more embodiments, the emissions forecasting system 102utilizes the forecasting machine-learning models 1102 a-1102 n togenerate forecasted source attributes 1104 a-1104 n for the physicalemissions sources 1100 a-1100 n. Specifically, the emissions forecastingsystem 102 generates forecasted source attributes for a particularphysical emissions source to indicate predicted source attributes for afuture time period including, but not limited to, a number of units ofthe physical emissions source and/or per-unit costs for the physicalemissions source during the future time period. In particular, theforecasting machine-learning models 1102 a-1102 n generate theforecasted source attributes based on learned characteristics ofhistorical data associated with the particular physical emissionssource.

For instance, the emissions forecasting system 102 utilizes the firstforecasting machine-learning model 1102 a to generate first forecastedsource attributes 1104 a indicating a number of units and/or per-unitcosts of the first physical emissions source 1100 a for the future timeperiod. To illustrate, the emissions forecasting system 102 utilizes thefirst forecasting machine-learning model 1102 a to generate a predictedprice-per-gallon of gasoline for a future time period based onhistorical prices-per-gallon of gasoline. Additionally, the emissionsforecasting system 102 utilizes the second forecasting machine-learningmodel 1102 b to generate second forecasted source attributes 1104 bindicating a number of units and/or per-unit costs of the secondphysical emissions source 1100 b for the future time period. Toillustrate, the emissions forecasting system 102 utilizes the secondforecasting machine-learning model 1102 b to generate a predictedprice-per-unit of electricity based on historical prices-per-unit ofelectricity.

In some embodiments, the emissions forecasting system 102 generates aplurality of different forecasted source attributes for each physicalemissions source for a plurality of different time periods. Toillustrate, the emissions forecasting system 102 generates a first setof forecasted source attributes for the plurality of physical emissionssources 1100 a-1100 n for a first time period. The emissions forecastingsystem 102 generates a second set of forecasted source attributes forthe plurality of physical emissions sources 1100 a-1100 n for a secondtime period In one or more embodiments, the emissions forecasting system102 generates the second set of forecasted source attributes based onthe first set of forecasted attributes. In alternative embodiments, theemissions forecasting system 102 generates the forecasted sourceattributes 1104 a-1104 n (e.g., a single set of forecasted sourceattributes) covering a plurality of time periods.

According to one or more embodiments, the emissions forecasting system102 determines weights 1106 a-1106 n for the forecasted sourceattributes 1104 a-1104 n based on relative importance. In particular,the emissions forecasting system 102 determines contribution proportionsof the physical emissions sources 1100 a-1100 n to various physicalemissions source attributes. For example, the emissions forecastingsystem 102 determines contribution proportions of the physical emissionssources 1100 a-1100 n to total emissions values and/or total costs forthe entity. The emissions forecasting system 102 determines the weights1106a-1106n based on the contribution proportions. Thus, if the firstphysical emissions source 1100 a has a highest contribution proportionrelative to total emissions, the emissions forecasting system 102determines a first weight 1106 a as the highest weight. Additionally, ifthe second physical emissions source 1100 b has a second highestcontribution proportion relative to total emissions, the emissionsforecasting system 102 determines a second highest weight 1106 b as thesecond highest weight (e.g., proportionally relative to the first weight1106 a).

After determining the weights 1106 a-1106 n (or otherwise aftergenerating the forecasted source attributes 1104 a-1104 n), theemissions forecasting system 102 generates forecasted emissions valuemodifications 1108. Specifically, the emissions forecasting system 102determines one or more modifications to one or more emissions values ofthe physical emissions sources 1100 a-1100 n based on the forecastedsource attributes 1104 a-1104 n (e.g., according to the weights 1106a-1106 n). To illustrate, the emissions forecasting system 102 generatesthe forecasted emissions value modifications 1108 to indicate a changein unit numbers and/or usage of specific physical emissions sources.More specifically, the forecasted emissions value modifications 1108include changes to one or more physical emissions sources while notchanging one or more other physical emissions sources. Additionally, bydetermining the forecasted emissions value modifications 1108 accordingto the weights 1106 a-1106 n, the emissions forecasting system 102determines changes to the physical emissions sources that are mostefficient for reducing emissions, costs, or other source attribute.

According to one or more embodiments, as illustrated in FIG. 11A, theemissions forecasting system 102 generates action recommendations 1110based on the forecasted emissions value modifications 1108. Inparticular, the emissions forecasting system 102 determines one or morepossible actions that the entity can perform to achieve specific targetemissions values based on the forecasted emissions value modifications1108. To illustrate, the emissions forecasting system 102 converts theforecasted emissions value modifications 1108 into natural languagerecommendations, as described with respect to FIG. 5 . Thus, theemissions forecasting system 102 converts data indicating a forecastedreduction of units/usage of one or more physical emissions sources by aspecific amount/percentage into a natural language phrase, sentence, orplurality of sentences. Additionally, as described, the emissionsforecasting system 102 further trains the natural language processingengine based on selected natural language recommendations.

As mentioned, in one or more embodiments, the emissions forecastingsystem 102 selects a plurality of different forecasting machine-learningmodels for a plurality of physical emissions sources. Thus, in someembodiments, the emissions forecasting system 102 selects a forecastingmachine-learning model for a particular physical emissions source from aplurality of available forecasting machine-learning models. Toillustrate, the emissions forecasting system 102 selects the forecastingmachine-learning model from a plurality of available forecastingmachine-learning models based on the accuracy/performance of theforecasting machine-learning models.

According to one or more embodiments, the emissions forecasting system102 utilizes ensemble forecasting to generate forecasted emissions datafor a physical emissions source. FIG. 11B illustrates that the emissionsforecasting system 102 selects a particular forecasting machine-learningmodel from a plurality of forecasting machine-learning models 1112a-1112 n for use with a particular physical emissions source. Forexample, the emissions forecasting system 102 determines the pluralityof forecasting machine-learning models 1112 a-1112 n based on one ormore attributes of the physical emissions source. To illustrate, theemissions forecasting system 102 determines one or more forecastingmachine-learning models commonly used for specific types of data,amounts of data, or desired output data.

In one or more embodiments, the emissions forecasting system 102utilizes the plurality of forecasting machine-learning models 1112a-1112 n to process training data and/or testing data associated withthe physical emissions source. For example, the physical emissionssource data 1114 associated with the physical emissions source. Morespecifically, the emissions forecasting system 102 determines historicaldata 1114 a for one or more past time periods for the physical emissionssource. As described previously, historical data for a physicalemissions source can include emissions values, costs, or other dataassociated with the physical emissions source. In additionalembodiments, the emissions forecasting system 102 utilizes a pluralityof training/testing datasets (e.g., historical data from a plurality ofdifferent past time periods and/or including different sourceattributes) for training/testing the forecasting machine-learning models1112 a-1112 n.

According to one or more embodiments, the emissions forecasting system102 utilizes the forecasting machine-learning models 1112 a-1112 n togenerate forecasted source attributes 1116 a-1116 n based on thetraining data. Specifically, the emissions forecasting system 102utilizes a first forecasting machine-learning model 1112 a to generatefirst forecasted source attributes 1116 a from the historical data 1114a. Additionally, the emissions forecasting system 102 utilizes a secondforecasting machine-learning model 1112 b to generate second forecastedsource attributes 1116 b from the historical data 1114 a. The emissionsforecasting system 102 thus utilizes the plurality of forecastingmachine-learning models 1112 a-1112 n to generate different sets offorecasted source attributes according to learned parameters of theforecasting machine-learning models 1112 a-1112 n based on the trainingdata.

The emissions forecasting system 102 determines the accuracy of theresults of the forecasting machine-learning models 1112 a-1112 n. Forinstance, as illustrated in FIG. 11B, the emissions forecasting system102 compares the forecasted source attributes 1116 a-1116 n toground-truth source attributes 1118 corresponding to the trainingdataset. To illustrate, the emissions forecasting system 102 determinesthe ground-truth source attributes 1118 based on measured sourceattributes for the physical emissions source. Accordingly, the emissionsforecasting system 102 compares the forecasted source attributes 1116a-1116 n to the ground-truth source attributes 1118 to determine theaccuracy of each forecasting machine-learning model.

After comparing the forecasted source attributes 1116 a-1116 n to theground-truth source attributes 1118, the emissions forecasting system102 determines a selected model 1120. Specifically, the emissionsforecasting system 102 selects a forecasting machine-learning model ofthe plurality of forecasting machine-learning models 1112 a-1112 n basedon the determined accuracies. For example, the emissions forecastingsystem 102 determines the selected model 1120 as the forecastingmachine-learning model with the highest accuracy (e.g., the smallestdifferences between the corresponding forecasted source attributes andthe ground-truth source attributes 1118). To illustrate, if the firstforecasting machine-learning model 1112 a produces the first forecastedsource attributes 1116 a closest to the ground-truth source attributes1118, the emissions forecasting system 102 determines the firstforecasting machine-learning model 1112 as the selected model 1120.

In response to determining the selected model 1120, the emissionsforecasting system 102 utilizes the selected model 1120 to forecastemissions data for the physical emissions source for a future timeperiod. In some embodiments, the emissions forecasting system 102utilizes the selected model 1120 with a plurality of additional selectedmodels for a plurality of physical emissions sources to generateforecasted emissions data for the plurality of physical emissionssources. The emissions forecasting system 102 can also determineselected models for one or more additional physical emissions sourceswithout utilizing the process illustrated in FIG. 11B (e.g., byselecting a model based on a mapping of source categories tomachine-learning models).

In additional embodiments, the emissions forecasting system 102determines one or more forecasting machine-learning models for one ormore additional physical emissions sources based on the selected model1120 and the physical emissions source data 1114. To illustrate, theemissions forecasting system 102 determines that the physical emissionssource is similar to one or more other physical emissions sources basedon attributes of the physical emissions source data 1114. The emissionsforecasting system 102 utilizes the selected model 1120 (e.g., aseparately trained instance) for an additional physical emissions sourceto generate forecasted emissions data for the additional physicalemissions source.

In one or more embodiments, after determining forecastingmachine-learning models to forecast data associated with a plurality ofphysical emissions sources, the emissions forecasting system 102monitors progress/performance of an entity in connection with theforecasted data. FIG. 11C illustrates that the emissions monitoringsystem 103 tracks an entity’s performance relative to forecasted data todetermine whether to provide additional recommendations to the entity.Specifically, FIG. 11C illustrates that the emissions monitoring system103 selectively determines whether to provide recommendations to modifyphysical emissions sources based on detected deviations from theforecasted data.

According to one or more embodiments, the emissions forecasting system102 generates forecasted emissions value modifications 1122 for aplurality of physical emissions sources utilizing a plurality offorecasting machine-learning models. In particular, the emissionsforecasting system 102 generates the forecasted emissions valuemodifications 1122 for modifying the physical emissions sources during afuture time period (e.g., by the end of the future time period). Theemissions forecasting system 102 can also provide a plan for modifyingthe physical emissions sources to achieve the forecasted emissions valuemodifications 1122 by the indicated time (e.g., by providing a timetablefor various modifications).

In connection with the emissions forecasting system 102 providing theforecasted emissions value modifications 1122 to the entity, theemissions monitoring system 103 also tracks the progress of the entity.In one or more embodiments, the emissions monitoring system 103determines tracked emissions values 1124 including emissions produced byan entity during the future time period corresponding to the forecastedemissions value modifications 1122. In particular, the emissionsmonitoring system 103 monitors the usage/production of emissions by theentity within a sub-time period of the future time period. Toillustrate, the emissions monitoring system 103 monitors the emissionsproduction of the entity (e.g., based on emissions parameters ofphysical emissions sources corresponding to the entity) during thesub-time period. As an example, the emissions monitoring system 103obtains electricity, natural gas, gasoline, or other usage of the entityfor the sub-time period, such as based on information provided directlyby the entity or by a third-party system.

In response to determining the tracked emissions values 1124, theemissions monitoring system 103 detects deviations from an emissionsplan generated based on the forecasted emissions value modifications1122. Specifically, the emissions monitoring system 103 determinesdifferences 1126 between the tracked emissions values 1124 and theforecasted emissions value modifications 1122. The emissions monitoringsystem 103 can determine the differences 1126 based on total emissionsvalues and/or individual emissions values for the individual physicalemissions sources. In one or more embodiments, the emissions monitoringsystem 103 determines whether the entity is ahead of, on track, orbehind a schedule corresponding to the emissions plan for modifying oneor more physical emissions sources to reduce emissions. To illustrate,if the entity has an emissions plan to reduce a unit number of aparticular physical emissions source by a certain value by the end ofthe future time period (or by the end of the sub-time period), theemissions monitoring system 103 determines whether the entity is ontrack to meet the planned unit number.

If the emissions monitoring system 103 determines that there is nodeviation from the plan (e.g., the corresponding difference is zero orpositive) for a particular physical emissions source or for the combinedphysical emissions sources, the emissions monitoring system 103determines that the entity is on track (e.g., ahead of or equal to theplanned value). If the emissions monitoring system 103 determines thatthere is a deviation from the plan (e.g., the corresponding differenceis negative), the emissions monitoring system 103 determines whether thedifference meets a threshold 1128 (e.g., a threshold emissionsdifference value). Specifically, the emissions monitoring system 103determines whether the differences 1126 between the forecasted emissionsvalue modifications 1122 and the tracked emissions values 1124 aresignificant enough to require adjustment to the plan. In some instances,minor differences may not require adjustment, while greater differencesmay benefit from adjustment to the plan to meet the forecasted emissionsvalue modifications 1122 by the end of the future time period.

In one or more embodiments, as illustrated in FIG. 11C, upon detecting adeviation of an entity from an emissions plan, the emissions monitoringsystem 103 generates action recommendations 1130. In particular, inresponse to determining that the differences 1126 meet the threshold1128, the emissions monitoring system 103 generates one or more actionrecommendations (e.g., alerts) to modify one or more physical emissionssources. For example, in response to determining that one or morephysical emissions sources caused the tracked emissions values 1124 todeviate from the forecasted emissions value modifications 1122 beyondthe bounds of the emissions plan, the emissions monitoring system 103generates one or more action recommendations with respect to thephysical emissions source(s). To illustrate, if the entity failed toreduce a unit number of a physical emissions source by a specific amountby a specific date, the emissions monitoring system 103 generates anaction recommendation to correct the deviation. Alternatively, theemissions monitoring system 103 generates an action recommendation tofurther reduce a different physical emissions source to make up for thedeviation in the physical emissions source.

In some embodiments, the emissions monitoring system 103 determines anaction to perform by increasing, decreasing, or otherwise modifying oneor more values of the emissions plan to more realistically achieve theforecasted emissions value modifications 1122. For instance, if theentity fails to perform certain actions previously recommended by aspecific date, the emissions monitoring system 103 modifies one or moretime periods in the emissions plan to accommodate the deviation (e.g.,rather than recommending correcting the deviation all at once). In someembodiments, the emissions monitoring system 103 also provides one ormore action recommendations to modify constraints, target emissionsvalues, or other parameters based on any deviations from the emissionsplan. In additional embodiments, the emissions monitoring system 103also utilizes one or more forecasting machine-learning models to updateforecasted data based on the deviations from initial forecasted data.

As previously described, the emissions forecasting system 102 generatesforecasted emissions data for physical emissions sources correspondingto an entity for one or more time periods. FIGS. 12A-12C illustratechart diagrams of the emissions forecasting system 102 processingphysical emissions source data for a plurality of physical emissionssources to generate forecasted emissions data. Additionally, FIGS.12A-12C illustrate that the emissions forecasting system 102 is able toflexibly generate forecasted data for a plurality of individual futuretime periods or for a combination of time periods.

FIG. 12A illustrates a chart diagram 1200 including a plurality ofphysical emissions sources corresponding to an entity. In one or moreembodiments, as previously noted with respect to FIGS. 8A-8F, physicalemissions sources correspond to various source categories based onemissions produced by the physical emissions sources. Additionally, thenumber and type of physical emissions sources are based on attributes ofthe entity including, but not limited to, the entity type (e.g.,restaurant, manufacturer, delivery service, financial entity), the sizeof the entity, or other details associated with the entity that affectthe emissions production. Furthermore, as illustrated in the chartdiagram 1200 includes source attributes such as unit costs, unit sizes,and unit type of each physical emissions source.

In one or more embodiments, the emissions forecasting system 102utilizes an optimizer system (e.g., the emissions optimizer system 112of FIG. 1 ) to generate optimized emissions for the entity.Specifically, FIG. 12B illustrates a chart diagram 1202 includingforecasted emissions data for a future time period based on emissionsresults generated by the emissions optimizer system (“WithOptimization”). The emissions forecasting system 102 utilizes theresults of the emissions optimizer system (e.g., an optimization of aprevious time period) as the baseline for generated forecasted emissionsdata for the plurality of physical emissions sources (e.g., theforecasted data is based on the optimized data).

Furthermore, in one or more embodiments, the emissions forecastingsystem 102 generates forecasted emissions data for the future timeperiod without optimization. In particular, as illustrated in the chartdiagram 1202 of FIG. 12B, the emissions forecasting system 102 generatesforecasted data without the use of an emissions optimizer system.Accordingly, for example, the emissions forecasting system 102 utilizesone or more constraints or goals provided for the entity to generate theforecasted emissions data. As illustrated, the emissions forecastingsystem 102 thus generates forecasted data that can vary depending on theinitial baseline (e.g., with optimized results as a baseline or withoutoptimized results as the baseline). In some embodiments, as illustrated,the emissions forecasting system 102 provides forecasted emissions datafor both the optimized and unoptimized cases.

FIG. 12C illustrates a chart diagram 1204 including forecasted emissionsdata for a plurality of future time periods. Specifically, the emissionsforecasting system 102 generates total forecasted emissions data for aplurality of future time periods (e.g., a plurality of years) utilizingforecasting machine-learning models for physical emissions sources of anentity. In one or more embodiments, the emissions forecasting system 102utilizes the forecasting machine-learning models to generate firstforecasted emissions data for a first future time period (e.g., anupcoming year). The emissions forecasting system 102 generates secondforecasted emissions data for a subsequent future time period (e.g., thenext year) based on the first forecasted emissions data (e.g., an inputto the forecasting machine-learning models for the second future timeperiod is based on an output of the first future time period). Theemissions forecasting system 102 provides the combined forecastedemissions for the first future time period and the second future timeperiod.

In additional embodiments, the emissions forecasting system 102 providesforecasted emissions data for a plurality of future time periods withdetails for the individual time periods. Accordingly, rather than (or inaddition to) presenting the combined forecasted emissions data for aplurality of future time periods, as in FIG. 12C, the emissionsforecasting system 102 provides a breakdown of forecasted emissions datafor each of the future time periods. To illustrate, the emissionsforecasting system 102 provides forecasted emissions data for a firstfuture time period and forecasted emissions data for a second futuretime period. In some embodiments, the emissions forecasting system 102provides the forecasted emissions data for one or more future timeperiods as part of an emissions plan that includes emissions valuemodifications based on the forecasted emissions data to allow an entityto view detailed information associated with the forecasted emissionsdata. Furthermore, the emissions plan can include action recommendationsfor individual future time periods and/or action recommendations for thecombined future time period (e.g., short-term actions and long-termactions).

FIGS. 13A-13B illustrate graphical user interfaces for forecasting andmonitoring emissions produced by an entity for one or more future timeperiods. Specifically, FIG. 13A illustrates a client device 1300presenting a graphical user interface of a client application 1302 forvarious entity management operations. For instance, the client device1300 displays a plurality of options for viewing information associatedwith past and forecasted emissions data. To illustrate, the clientdevice 1300 displays historical emissions values 1304 for a plurality ofphysical emissions sources corresponding to an entity. The client device1300 can display, in response to an interaction with a graphical userinterface element, the historical emissions values 1304 to allow theentity to view physical emissions source data or other parametersassociated with emissions produced by the entity.

Additionally, as illustrated in FIG. 13A, the emissions forecastingsystem 102 generates forecasted emissions data for a plurality of futuretime periods. In one or more embodiments, the client device 1300displays first forecasted emissions data 1306 a for a first future timeperiod, second forecasted emissions data 1306 b for a second future timeperiod, and combined forecasted emissions data 1306 c for a future timeperiod combining the first future time period and the second future timeperiod. In response to interactions with one or more graphical userinterface elements, the client device 1300 displays details associatedwith the forecasted emissions data for the one or more future timeperiods. Additionally, although FIG. 13A illustrates that the clientdevice 1300 displays forecasted emissions data for a plurality of futuretime periods, the client device 1300 can display forecasted emissionsdata for a single future time period at a time.

In one or more embodiments, the emissions forecasting system 102 alsoprovides action recommendations via a client device. For example, asillustrated in FIG. 13A, the client device 1300 displays a plurality ofaction recommendations 1308 a-1308 c for each scenario (e.g., each timeperiod or combined time period). To illustrate, the emissionsforecasting system 102 generates first action recommendations 1308 a forthe first future time period, second action recommendations 1308 b forthe second future time period, and third action recommendations 1308 cfor the combined future time periods. The client device 1300 displaysthe action recommendations with the corresponding forecasted emissionsdata. Furthermore, in response to interactions with one or moregraphical user interface elements, the client device 1300 displaysadditional information associated with the action recommendations, suchas timelines, predicted impacts, etc.

In addition to presenting forecasted emissions data and actionrecommendations based on the forecasted emissions data, the emissionsforecasting system 102 also provides data associated with monitoringprogress of an entity in connection with an emissions plan. For example,FIG. 13B illustrates that the client device 1300 displays informationbased on tracked performance of the entity with respect to an emissionsplan. To illustrate, the client device 1300 displays an emissions plan1310 including one or more previously recommended actions, forecastedemissions data, timelines, or other data associated with modifyingphysical emissions sources of an entity for one or more future timeperiods (e.g., any information displayed on the client device 1300 inFIG. 13A).

Additionally, the emissions forecasting system 102 tracks emissions dataassociated with an entity after an entity implements an emissions plan.For example, after an entity has selected an emissions plant (e.g., byselecting a graphical user interface element including an emissions planwith one or more action recommendations for modifying physical emissionssources), the emissions forecasting system 102 tracks the entity’semissions production. To illustrate, the emissions forecasting system102 tracks physical emissions source data at regular intervals to detectany deviations from the emissions plan. As illustrated in FIG. 13B, theclient device 1300 displays tracked differences 1312 includingdeviations from the emissions plan 1310 based on the tracked emissionsdata for the entity. This allows the entity to view modifications oractions that the entity has performed or that the entity still needs toperform according to the emissions plan 1310.

In one or more embodiments, the emissions forecasting system 102 alsogenerate action recommendations 1314 (e.g., as a real-time alert) basedon the tracked differences 1312. For instance, in response todetermining that the tracked differences 1312 meet a threshold (e.g.,exceed an emissions difference threshold) for one or more physicalemissions sources, the emissions forecasting system 102 generates theaction recommendations 1314 to modify (or further modify) one or morephysical emissions sources to reduce the differences or correct adeviation. FIG. 13B illustrates that the client device 1300 displays theaction recommendations 1314. In some embodiments, the actionrecommendations 1314 include interactive graphical user interfaceelements for selecting, implementing, or tracking the actionrecommendations.

According to one or more embodiments, the emissions forecasting system102 detects interactions with one or more action recommendations formodifying physical emissions sources according to the trackeddifferences 1312. For instance, in response to the client device 1300detecting an interaction with the action recommendations 1314, theemissions forecasting system 102 generates instructions to provide toone or more source modification devices. To illustrate, the emissionsforecasting system 102 determines one or more emissions valuemodifications based on the emissions plan 1310 and the trackeddifferences 1312. The emissions forecasting system 102 generatesinstructions to modify one or more physical emissions sources accordingto the action recommendations 1314 and provides the instructions to thesource modification devices to apply one or more changes to the physicalemissions sources. Specifically, the emissions forecasting system 102updates control settings associated with the physical emissions sourcesto limit usage/time based on a modified source attribute correspondingto the action recommendations 1314.

In additional embodiments, the emissions forecasting system 102 utilizesthe tracked differences 1312 to automatically implement modifications tothe physical emissions sources. For example, the emissions forecastingsystem 102 utilizes the tracked differences 1312 to determine deviationsfrom the emissions plan 1310. The emissions forecasting system 102determines one or more actions to modify the physical emissions sourcesand reduce the differences during a remaining time period associatedwith the emissions plan 1310. Additionally, the emissions forecastingsystem 102 generates instructions for modifying the physical emissionssources based on the determined action(s) and provides the instructionsto the source modification device(s) to modify the physical emissionssources. The emissions forecasting system 102 thus automaticallyimplements action recommendations to adjust performance/usage ofphysical emissions sources based on user selections and/orforecasted/monitored changes in source attributes.

Turning now to FIG. 14 , this figure shows a flowchart of a series ofacts 1400 of generating action recommendations for modifying physicalemissions sources based on forecasted emissions usage utilizing aplurality of forecasting machine-learning models. While FIG. 14illustrates acts according to one embodiment, alternative embodimentsmay omit, add to, reorder, and/or modify any of the acts shown in FIG.14 . The acts of FIG. 14 can be performed as part of a method.Alternatively, a non-transitory computer readable medium can compriseinstructions, that when executed by one or more processors, cause acomputing device to perform the acts of FIG. 14 . In still furtherembodiments, a system can perform the acts of FIG. 14 .

As shown, the series of acts 1400 includes an act 1402 of generatingforecasted source attributes for physical emissions sources. Forexample, act 1402 involves generating, utilizing a plurality offorecasting machine-learning models, a plurality of forecasted sourceattributes for a plurality of physical emissions sources correspondingto an entity for a future time period according to a plurality ofconstraints and historical data associated with the entity. In one ormore embodiments, the emissions forecasting system 102 utilizesforecasting machine-learning models to perform act 1402, as describedabove with respect to FIGS. 1, 10, and 11A.

As part of act 1402, or as an additional act, the series of acts 1400includes determining a plurality of forecasting machine-learning modelsfor a plurality of physical emissions sources based on historical dataassociated with the plurality of physical emissions sources. Forexample, the series of acts 1400 includes determining attributes ofhistorical data associated with a physical emissions source of theplurality of physical emissions sources. The series of acts 1400 canfurther include determining a forecasting machine-learning model for thephysical emissions source based on the attributes of the historical dataassociated with the physical emissions source. The series of acts 1400can also include determining a second forecasting machine-learning modelbased on attributes of historical data associated with a second physicalemissions source of the plurality of physical emissions sources. Theseries of acts 1400 can include generating the first set of forecastedsource attributes comprising first forecasted source attributes of thefirst physical emissions source utilizing the first forecastingmachine-learning model and second forecasted source attributes of thesecond physical emissions source utilizing the second forecastingmachine-learning model.

The series of acts 1400 can include generating, utilizing a plurality ofmachine-learning models, a plurality of sets of forecasted sourceattributes for the physical emissions source for the historical data.The series of acts 1400 can include selecting the forecastingmachine-learning model based on the plurality of sets of forecastedsource attributes.

Act 1402 can involve generating, utilizing a first forecastingmachine-learning model, a first set of forecasted source attributes fora first physical emissions source of the plurality of physical emissionssources. For example, act 1402 can involve determining the firstforecasting machine-learning model based on historical data associatedwith the first physical emissions source. Act 1402 can also involvegenerating, utilizing a second forecasting machine-learning model, asecond set of forecasted source attributes for a second physicalemissions source of the plurality of physical emissions sources. Act1402 can further involve determining the second forecastingmachine-learning model based on historical data associated with thesecond physical emissions source, the second forecastingmachine-learning model being different than the first forecastingmachine-learning model.

Act 1402 can involve generating, utilizing the plurality of forecastingmachine-learning models, a first subset of forecasted source attributesfor a first time period. For example, act 1402 can involve generating,utilizing a modified gradient descent model, an initial set of sourceattributes for the plurality of physical emissions sources according tothe plurality of constraints and one or more target emissions values.Act 1402 can involve generating the initial set of source attributesbased on historical data for a previous time period. Act 1402 caninvolve generating the first subset of forecasted source attributes forthe first time period based on the initial set of source attributesgenerated by the modified gradient descent model. Alternatively, act1402 can involve determining the first subset of forecasted sourceattributes for the first time period based on the plurality ofconstraints. Act 1402 can involve generating, utilizing the plurality offorecasting machine-learning models, a first subset of forecasted sourceattributes comprising per-unit emissions values and per-unit costs ofthe plurality of physical emissions sources for a first time period.

Act 1402 can also involve generating, utilizing the plurality offorecasting machine-learning models, a second subset of forecastedsource attributes for a second time period based on the first subset offorecasted source attributes of the first time period. For example, act1402 can involve generating, utilizing the plurality of forecastingmachine-learning models, a second subset of forecasted source attributescomprising per-unit emissions values and per-unit costs of the pluralityof physical emissions sources for a second time period based on thefirst subset of forecasted source attributes of the first time period.

The series of acts 1400 also includes an act 1404 of determiningforecasted emissions value modifications from the forecasted sourceattributes. For example, act 1404 involves determining a plurality offorecasted emissions value modifications for the plurality of physicalemissions sources based on the plurality of forecasted source attributesfor the future time period. Act 1404 can also involve determining theplurality of forecasted emissions value modifications based on theplurality of constraints. In one or more embodiments, the emissionsforecasting system 102 utilizes forecasting machine-learning models toperform act 1404, as described above with respect to FIGS. 1, 10, and11A.

Act 1404 can involve determining weights associated with the pluralityof physical emissions sources based on contribution proportions of theplurality of physical emissions sources and the plurality of constraintsof the entity. Act 1404 can involve determining the plurality offorecasted emissions value modifications based on the weights associatedwith the plurality of physical emissions sources.

Act 1404 can involve determining a first weight associated with thefirst set of forecasted source attributes based on the first physicalemissions source. Act 1404 can also involve determining a second weightassociated with the second set of forecasted source attributes based onthe second physical emissions source. Act 1404 can involve determiningthe plurality of forecasted emissions value modifications based on thefirst weight associated with the first set of forecasted sourceattributes and the second weight associated with the second set offorecasted source attributes.

Additionally, the series of acts 1400 includes an act 1406 of generatingaction recommendations for modifying the physical emissions sources. Forexample, act 1406 involves generating one or more action recommendationsfor modifying the plurality of physical emissions sources for the entitybased on the plurality of forecasted emissions value modifications forthe plurality of physical emissions sources. In one or more embodiments,the emissions forecasting system 102 utilizes forecastingmachine-learning models to perform act 1406, as described previouslywith respect to FIGS. 1, 9, 10, and 11A.

Act 1406 can involve generating, utilizing a natural language processingengine, a natural language action recommendation indicating amodification to one or more emissions values for at least one physicalemissions source of the plurality of physical emissions sources. Act1406 can also involve receiving a selection of the natural languageaction recommendation from a plurality of natural languagerecommendations generated utilizing the natural language processingengine. Act 1408 can involve learning parameters of the natural languageprocessing engine based on the selection of the natural language actionrecommendation.

The series of acts 1400 further includes an act 1408 of providing theaction recommendations for display within a graphical user interface.For example, act 1408 involves providing the one or more actionrecommendations for display via a graphical user interface of a clientdevice of the entity. Act 1408 can involve providing the one or moreaction recommendations for a plurality of future time periods. Act 1408can involve providing the one or more action recommendations for acombined future time period corresponding to a plurality of future timeperiods. Act 1408 can also involve providing the one or more actionrecommendations for display with historical data associated with theplurality of physical emissions sources and forecasted emissions datafor a future time period. In one or more embodiments, the emissionsforecasting system 102 or the entity management system 110 performs act1408, as described above with respect to FIGS. 1, 13A, and 13B.

In one or more embodiments, the series of acts 1400 also includestracking, during the future time period, emissions values correspondingto the plurality of physical emissions sources of the entity. The seriesof acts 1400 includes determining differences between the trackedemissions values and the plurality of forecasted emissions valuemodifications. Additionally, the series of acts 1400 includes generatingone or more additional recommendations to modify the plurality ofphysical emissions sources for the entity based on the differencesbetween the tracked emissions values and the plurality of forecastedemissions value modifications.

The series of acts 1400 can include tracking, for a sub-time periodduring the first time period, emissions values for the plurality ofphysical emissions sources corresponding to the entity. The series ofacts 1400 can include generating, for the sub-time period, one or moreaction recommendations to modify the plurality of physical emissionssources in response to determining that differences between the trackedemissions values and the plurality of forecasted emissions valuemodifications meet a threshold.

In one or more embodiments, the series of acts 1400 includes modifyingone or more physical emissions sources modifies, utilizing one or moresource modification devices, one or more physical emissions sources ofthe plurality of physical emissions sources based on an actionrecommendation of the one or more action recommendations. For example,the series of acts 1400 includes generating instructions for modifyingone or more physical emissions sources of the plurality of physicalemissions sources based on a selected action recommendation of the oneor more action recommendations. The series of acts 1400 also includesmodifying, utilizing the one or more service modification devices, oneor more control settings associated with the one or more physicalemissions sources that limits usage of the one or more physicalemissions sources according to the instructions.

Embodiments of the present disclosure may comprise or utilize a specialpurpose or general-purpose computer including computer hardware, suchas, for example, one or more processors and system memory, as discussedin greater detail below. Embodiments within the scope of the presentdisclosure also include physical and other computer-readable media forcarrying or storing computer-executable instructions and/or datastructures. In particular, one or more of the processes described hereinmay be implemented at least in part as instructions embodied in anon-transitory computer-readable medium and executable by one or morecomputing devices (e.g., any of the media content access devicesdescribed herein). In general, a processor (e.g., a microprocessor)receives instructions, from a non-transitory computer-readable medium,(e.g., a memory, etc.), and executes those instructions, therebyperforming one or more processes, including one or more of the processesdescribed herein.

Computer-readable media can be any available media that can be accessedby a general purpose or special purpose computer system.Computer-readable media that store computer-executable instructions arenon-transitory computer-readable storage media (devices).Computer-readable media that carry computer-executable instructions aretransmission media. Thus, by way of example, and not limitation,embodiments of the disclosure can comprise at least two distinctlydifferent kinds of computer-readable media: non-transitorycomputer-readable storage media (devices) and transmission media.

Non-transitory computer-readable storage media (devices) includes RAM,ROM, EEPROM, CD-ROM, solid state drives (“SSDs”) (e.g., based on RAM),Flash memory, phase-change memory (“PCM”), other types of memory, otheroptical disk storage, magnetic disk storage or other magnetic storagedevices, or any other medium which can be used to store desired programcode means in the form of computer-executable instructions or datastructures and which can be accessed by a general purpose or specialpurpose computer.

A “network” is defined as one or more data links that enable thetransport of electronic data between computer systems and/or modulesand/or other electronic devices. When information is transferred orprovided over a network or another communications connection (eitherhardwired, wireless, or a combination of hardwired or wireless) to acomputer, the computer properly views the connection as a transmissionmedium. Transmissions media can include a network and/or data linkswhich can be used to carry desired program code means in the form ofcomputer-executable instructions or data structures and which can beaccessed by a general purpose or special purpose computer. Combinationsof the above should also be included within the scope ofcomputer-readable media.

Further, upon reaching various computer system components, program codemeans in the form of computer-executable instructions or data structurescan be transferred automatically from transmission media tonon-transitory computer-readable storage media (devices) (or viceversa). For example, computer-executable instructions or data structuresreceived over a network or data link can be buffered in RAM within anetwork interface module (e.g., a “NIC”), and eventually transferred tocomputer system RAM and/or to less volatile computer storage media(devices) at a computer system. Thus, it should be understood thatnon-transitory computer-readable storage media (devices) can be includedin computer system components that also (or even primarily) utilizetransmission media.

Computer-executable instructions comprise, for example, instructions anddata which, when executed at a processor, cause a general-purposecomputer, special purpose computer, or special purpose processing deviceto perform a certain function or group of functions. In someembodiments, computer-executable instructions are executed on ageneral-purpose computer to turn the general-purpose computer into aspecial purpose computer implementing elements of the disclosure. Thecomputer executable instructions may be, for example, binaries,intermediate format instructions such as assembly language, or evensource code. Although the subject matter has been described in languagespecific to structural features and/or methodological acts, it is to beunderstood that the subject matter defined in the appended claims is notnecessarily limited to the described features or acts described above.Rather, the described features and acts are disclosed as example formsof implementing the claims.

Those skilled in the art will appreciate that the disclosure may bepracticed in network computing environments with many types of computersystem configurations, including, personal computers, desktop computers,laptop computers, message processors, hand-held devices, multiprocessorsystems, microprocessor-based or programmable consumer electronics,network PCs, minicomputers, mainframe computers, mobile telephones,PDAs, tablets, pagers, routers, switches, and the like. The disclosuremay also be practiced in distributed system environments where local andremote computer systems, which are linked (either by hardwired datalinks, wireless data links, or by a combination of hardwired andwireless data links) through a network, both perform tasks. In adistributed system environment, program modules may be located in bothlocal and remote memory storage devices.

Embodiments of the present disclosure can also be implemented in cloudcomputing environments. In this description, “cloud computing” isdefined as a model for enabling on-demand network access to a sharedpool of configurable computing resources. For example, cloud computingcan be employed in the marketplace to offer ubiquitous and convenienton-demand access to the shared pool of configurable computing resources.The shared pool of configurable computing resources can be rapidlyprovisioned via virtualization and released with low management effortor service provider interaction, and scaled accordingly.

A cloud-computing model can be composed of various characteristics suchas, for example, on-demand self-service, broad network access, resourcepooling, rapid elasticity, measured service, and so forth. Acloud-computing model can also expose various service models, such as,for example, Software as a Service (“SaaS”), Platform as a Service(“PaaS”), and Infrastructure as a Service (“IaaS”). A cloud-computingmodel can also be deployed using different deployment models such asprivate cloud, community cloud, public cloud, hybrid cloud, and soforth. In this description and in the claims, a “cloud-computingenvironment” is an environment in which cloud computing is employed.

FIG. 15 illustrates a block diagram of exemplary computing device 1500that may be configured to perform one or more of the processes describedabove. One will appreciate that one or more computing devices such asthe computing device 1500 may implement the system(s) of FIG. 1 . Asshown by FIG. 15 , the computing device 1500 can comprise a processor1502, a memory 1504, a storage device 1506, an I/O interface 1508, and acommunication interface 1510, which may be communicatively coupled byway of a communication infrastructure 1512. In certain embodiments, thecomputing device 1500 can include fewer or more components than thoseshown in FIG. 15 . Components of the computing device 1500 shown in FIG.15 will now be described in additional detail.

In one or more embodiments, the processor 1502 includes hardware forexecuting instructions, such as those making up a computer program. Asan example, and not by way of limitation, to execute instructions fordynamically modifying workflows, the processor 1502 may retrieve (orfetch) the instructions from an internal register, an internal cache,the memory 1504, or the storage device 1506 and decode and execute them.The memory 1504 may be a volatile or non-volatile memory used forstoring data, metadata, and programs for execution by the processor(s).The storage device 1506 includes storage, such as a hard disk, flashdisk drive, or other digital storage device, for storing data orinstructions for performing the methods described herein.

The I/O interface 1508 allows a user to provide input to, receive outputfrom, and otherwise transfer data to and receive data from computingdevice 1500. The I/O interface 1508 may include a mouse, a keypad or akeyboard, a touch screen, a camera, an optical scanner, networkinterface, modem, other known I/O devices or a combination of such I/Ointerfaces. The I/O interface 1508 may include one or more devices forpresenting output to a user, including, but not limited to, a graphicsengine, a display (e.g., a display screen), one or more output drivers(e.g., display drivers), one or more audio speakers, and one or moreaudio drivers. In certain embodiments, the I/O interface 1508 isconfigured to provide graphical data to a display for presentation to auser. The graphical data may be representative of one or more graphicaluser interfaces and/or any other graphical content as may serve aparticular implementation.

The communication interface 1510 can include hardware, software, orboth. In any event, the communication interface 1510 can provide one ormore interfaces for communication (such as, for example, packet-basedcommunication) between the computing device 1500 and one or more othercomputing devices or networks. As an example, and not by way oflimitation, the communication interface 1510 may include a networkinterface controller (NIC) or network adapter for communicating with anEthernet or other wire-based network or a wireless NIC (WNIC) orwireless adapter for communicating with a wireless network, such as aWI-FI.

Additionally, the communication interface 1510 may facilitatecommunications with various types of wired or wireless networks. Thecommunication interface 1510 may also facilitate communications usingvarious communication protocols. The communication infrastructure 1512may also include hardware, software, or both that couples components ofthe computing device 1500 to each other. For example, the communicationinterface 1510 may use one or more networks and/or protocols to enable aplurality of computing devices connected by a particular infrastructureto communicate with each other to perform one or more aspects of theprocesses described herein. To illustrate, the digital content campaignmanagement process can allow a plurality of devices (e.g., a clientdevice and server devices) to exchange information using variouscommunication networks and protocols for sharing information such aselectronic messages, user interaction information, engagement metrics,or campaign management resources.

In the foregoing specification, the present disclosure has beendescribed with reference to specific exemplary embodiments thereof.Various embodiments and aspects of the present disclosure(s) aredescribed with reference to details discussed herein, and theaccompanying drawings illustrate the various embodiments. Thedescription above and drawings are illustrative of the disclosure andare not to be construed as limiting the disclosure. Numerous specificdetails are described to provide a thorough understanding of variousembodiments of the present disclosure.

The present disclosure may be embodied in other specific forms withoutdeparting from its spirit or essential characteristics. The describedembodiments are to be considered in all respects only as illustrativeand not restrictive. For example, the methods described herein may beperformed with less or more steps/acts or the steps/acts may beperformed in differing orders. Additionally, the steps/acts describedherein may be repeated or performed in parallel with one another or inparallel with different instances of the same or similar steps/acts. Thescope of the present application is, therefore, indicated by theappended claims rather than by the foregoing description. All changesthat come within the meaning and range of equivalency of the claims areto be embraced within their scope.

What is claimed is:
 1. A computer-implemented method comprising:determining, by at least one processor utilizing a plurality offorecasting machine-learning models, a plurality of forecasted emissionsvalue modifications for a plurality of physical emissions sources basedon a plurality of forecasted source attributes and a constraint on anumber of physical objects corresponding to the plurality of physicalemissions sources; generating, by the at least one processor, an actionrecommendation for modifying a physical emissions source of theplurality of physical emissions sources based on the plurality offorecasted emissions value modifications; and causing, by the at leastone processor according to the action recommendation, a sourcemodification device to modify a control setting that limits usage of thephysical emissions source of the plurality of physical emissionssources.
 2. The computer-implemented method of claim 1, furthercomprising generating the plurality of forecasted source attributes by:generating, utilizing a first forecasting machine-learning model, afirst set of forecasted source attributes for a first physical emissionssource of the plurality of physical emissions sources; and generating,utilizing a second forecasting machine-learning model, a second set offorecasted source attributes for a second physical emissions source ofthe plurality of physical emissions sources.
 3. The computer-implementedmethod of claim 2, wherein generating the plurality of forecasted sourceattributes comprises: selecting the first forecasting machine-learningmodel for the first physical emissions source based on attributes ofhistorical data associated with the first physical emissions source; andselecting the second forecasting machine-learning model for the secondphysical emissions source based on attributes of historical dataassociated with the second physical emissions source, the secondforecasting machine-learning model being different than the firstforecasting machine-learning model.
 4. The computer-implemented methodof claim 1, wherein generating the plurality of forecasted sourceattributes comprises: generating a first subset of forecasted sourceattributes for a first time period; and generating a second subset offorecasted source attributes for a second time period based on the firstsubset of forecasted source attributes of the first time period.
 5. Thecomputer-implemented method of claim 4, wherein generating the firstsubset of forecasted source attributes comprises: generating, utilizinga modified gradient descent model, an initial set of source attributesfor the plurality of physical emissions sources according to theconstraint on the number of physical objects and one or more targetemissions values; and generating the first subset of forecasted sourceattributes for the first time period utilizing the initial set of sourceattributes by the modified gradient descent model.
 6. Thecomputer-implemented method of claim 1, wherein generating the actionrecommendation comprises determining an operating limit for an attributeof the physical emissions source based on the plurality of forecastedemissions value modifications.
 7. The computer-implemented method ofclaim 6, wherein causing the source modification device to modify thecontrol setting comprises causing the source modification device toimplement the operating limit for the attribute of the physicalemissions source via the control setting.
 8. The computer-implementedmethod of claim 7, causing the source modification device to modify thecontrol setting comprises causing the source modification device tomodify an operating temperature, an operating speed, an operating power,or an operating time of the physical emissions source.
 9. Thecomputer-implemented method of claim 1, further comprising: tracking,during a future time period corresponding to the plurality of forecastedemissions value modifications, emissions values for the plurality ofphysical emissions sources; determining differences between the trackedemissions values during the future time period and the plurality offorecasted emissions value modifications; and generating an additionalaction recommendation to modify to modify one or more physical emissionssources of the plurality of physical emissions sources according to thedifferences between the tracked emissions values and the plurality offorecasted emissions value modifications.
 10. A system comprising: oneor more memory devices; and one or more processors configured to causethe system to: determine, utilizing a plurality of forecastingmachine-learning models, a plurality of forecasted emissions valuemodifications for a plurality of physical emissions sources based on aplurality of forecasted source attributes for a future time period and aconstraint on a number of physical objects corresponding to theplurality of physical emissions sources; generate an actionrecommendation for modifying a physical emissions source of theplurality of physical emissions sources based on the plurality offorecasted emissions value modifications for the plurality of physicalemissions sources; and cause, according to the action recommendation, asource modification device to modify a control setting that limits usageof a physical emissions source of the plurality of physical emissionssources.
 11. The system of claim 10, wherein the one or more processorsare configured to cause the system to determine the plurality offorecasting machine-learning models for the plurality of physicalemissions sources by: determining an amount or a type of historical dataassociated with a particular physical emissions source of the pluralityof physical emissions sources; and determining, from a plurality ofmachine-learning models, a forecasting machine-learning model for theparticular physical emissions source based on the amount or the type ofthe historical data associated with the particular physical emissionssource.
 12. The system as recited in claim 11, wherein the one or moreprocessors are configured to cause the system to determine theforecasting machine-learning model for the particular physical emissionssource by: generating, utilizing the plurality of machine-learningmodels, a plurality of sets of forecasted source attributes for theparticular physical emissions source in connection with the historicaldata; and selecting, from the plurality of machine-learning models, theforecasting machine-learning model for the particular physical emissionssource based on the plurality of sets of forecasted source attributesrelative to a set of ground-truth source attributes for the particularphysical emissions source.
 13. The system as recited in claim 11,wherein the one or more processors are configured to cause the system togenerate the plurality of forecasted source attributes by: generating,utilizing the plurality of forecasting machine-learning models, a firstsubset of forecasted source attributes comprising per-unit emissionsvalues and per-unit costs of the plurality of physical emissions sourcesfor a first time period; and generating, utilizing the plurality offorecasting machine-learning models, a second subset of forecastedsource attributes comprising per-unit emissions values and per-unitcosts of the plurality of physical emissions sources for a second timeperiod based on the first subset of forecasted source attributes of thefirst time period.
 14. The system as recited in claim 13, wherein theone or more processors are configured to cause the system to generatethe action recommendation by determining a modification of the physicalemissions source to achieve one or more target emissions values for thesecond time period based on the second subset of forecasted sourceattributes.
 15. The system as recited in claim 14, wherein the one ormore processors are configured to cause the system to cause the sourcemodification device to modify the control setting by causing the sourcemodification device to implement, via the control setting, themodification of the physical emissions source.
 16. The system as recitedin claim 10, wherein the one or more processors are configured to causethe system to determine the plurality of forecasted emissions valuemodifications for the plurality of physical emissions sources by:determining contribution proportions of a first physical emissionssource and a second physical emissions source to the plurality offorecasted source attributes; determining a first weight associated withthe first physical emissions source and a second weight associated withthe second physical emissions source based on the contributionproportions; and determining the plurality of forecasted emissions valuemodifications based on the first weight and the second weight.
 17. Thesystem as recited in claim 10, wherein the one or more processors areconfigured to cause the system to: track emissions values correspondingto the plurality of physical emissions sources during a future timeperiod; and generate an additional action recommendation to furthermodify the control setting that limits the usage of the physicalemissions source based on the tracked emissions values relative to theplurality of forecasted emissions value modifications.
 18. Acomputer-implemented method comprising: instructing a computing systemto generate an action recommendation for modifying a plurality ofphysical emissions sources, wherein the computing system generates theaction recommendation by: determining, utilizing a plurality offorecasting machine-learning models, a plurality of forecasted emissionsvalue modifications for the plurality of physical emissions sourcesbased on a plurality of forecasted source attributes for a future timeperiod and a constraint on a number of physical objects corresponding tothe plurality of physical emissions sources; and generating the actionrecommendation for modifying a physical emissions source of theplurality of physical emissions sources based on the plurality offorecasted emissions value modifications for the plurality of physicalemissions sources; and modifying, according to the actionrecommendation, a control setting that limits usage of a physicalemissions source of the plurality of physical emissions sources.
 19. Thecomputer-implemented method as recited in claim 18, wherein instructingthe computing system to generate the action recommendation comprises:determining the constraint on the number of physical objectscorresponding to the plurality of physical emissions sources in responseto detecting one or more inputs via one or more graphical user interfaceelements corresponding to the plurality of physical emissions sources;and sending a request to the computing system to generate the actionrecommendation for modifying the plurality of physical emissions sourcesaccording to the constraint.
 20. The computer-implemented method asrecited in claim 18, wherein modifying the control setting comprisesmodifying an operating temperature, an operating speed, an operatingpower, or an operating time of the physical emissions source in responseto a selection of the action recommendation via a graphical userinterface.